Setup

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Data preparation

NAs introduced by coercionNAs introduced by coercionNAs introduced by coercionNAs introduced by coercionNAs introduced by coercionNAs introduced by coercion‘-’ not meaningful for factorsJoining, by = c("study", "subid", "target", "happy", "depressed", "fear", "angry", "calm", "sounds", "seeing", "temperature", "odors", "depth", "computations", "thoughts", "reasoning", "remembering", "beliefs", "hungry", "tired", "pain", "nauseated", "safe", "love", "recognizing", "communicating", "guilt", "disrespected", "free_will", "choices", "self_restraint", "intentions", "goal", "conscious", "self_aware", "desires", "embarrassed", "emo_recog", "joy", "morality", "personality", "pleasure", "pride", "CATCH")
Joining, by = c("study", "subid")

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Analysis plan

For all studies we conduct exploratory factor analyses using Pearson correlations to find minimum residual solutions.

For each study, we first examine maximal unrotated and rotated solutions. To determine the maximum number of factors to extract, we use the following rule of thumb: With \(p\) observations per participant, we can extract a maximum of \(k\) factors, where \((p-k)*2 > p+k\), i.e., \(k < p/3\). Thus, with 40 mental capacity items, we can extract a maximum of 13 factors.

To determine how many factors to retain, we use the following preset retention criteria, considering the unrotated maximal solution:

We then examine and interpret varimax-rotated solutions, extracting only the number of factors that meet these criteria.

Note: For Studies 1-2, we initially planned to conduct dimension reduction analyses for each condition (beetle vs. robot) separately. However, we now consider this analysis plan to have been fundamentally flawed: Each of these separate analyses is only capable of surfacing factors that highlight substantial disagreement among participants within that condition thus failing to capture key differences in attributions of mental capacities to beetles vs. robots, with no formal means of synthesizing results across conditions. Nonetheless, the results of these analyses are generally consistent with the findings reported here: The most prominent and reliable finding within each condition is that participants distinguish between emotional and perceptual varieties of experience. See https://osf.io/zd3mu for the preregistered analyses, included analysis scripts.

Study 1

Design: 2 conditions (beetle, robot), between-subjects Date conducted: 2015-12-15

Demographics

NAs introduced by coercion
Joining, by = c("condition", "min_age", "max_age", "median_age", "mean_age", "sd_age")
joining character vector and factor, coercing into character vector

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Exploratory factor analysis

Step 1: Run maximal EFA (without and with rotation)

Parallel analysis suggests that the number of factors =  3  and the number of components =  3 

Factor Analysis using method =  minres
Call: fa(r = d1_all, nfactors = 13, rotate = "none", fm = "minres", 
    cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
                 MR1   MR2   MR4   MR3   MR6   MR5   MR7   MR8   MR9  MR11  MR10  MR12  MR13   h2    u2 com
happy           0.78  0.26 -0.08  0.29 -0.06 -0.20 -0.07  0.04  0.04  0.01  0.02  0.04 -0.06 0.82 0.179 1.8
depressed       0.75  0.38 -0.21  0.10 -0.11  0.01  0.03  0.10  0.04 -0.04 -0.04  0.09 -0.07 0.80 0.196 1.9
fear            0.74 -0.17  0.08  0.41  0.03  0.11 -0.12  0.09  0.02 -0.04  0.09 -0.01  0.00 0.80 0.202 1.9
angry           0.77  0.15 -0.05  0.23 -0.06  0.02 -0.01  0.16  0.08 -0.01  0.08  0.05  0.02 0.71 0.286 1.5
calm            0.74  0.03  0.11  0.25  0.11  0.00 -0.13  0.00  0.12 -0.01  0.03  0.05  0.00 0.67 0.325 1.5
sounds          0.11  0.16  0.59 -0.06 -0.30 -0.05 -0.14 -0.12  0.16  0.07  0.07  0.04  0.08 0.56 0.440 2.4
seeing          0.27  0.01  0.64  0.16 -0.25  0.09  0.04 -0.05 -0.04 -0.10  0.01  0.04  0.02 0.60 0.404 2.0
temperature     0.18  0.11  0.68  0.08 -0.25  0.06 -0.06 -0.03 -0.11  0.19  0.05  0.12  0.12 0.66 0.342 2.0
odors           0.37 -0.07  0.47  0.17 -0.24  0.06  0.01 -0.13  0.09 -0.06 -0.09  0.12  0.07 0.51 0.492 3.6
depth           0.22  0.25  0.53 -0.02 -0.11  0.01  0.04 -0.03 -0.03  0.09  0.04 -0.07  0.00 0.42 0.577 2.1
computations   -0.35  0.66  0.22 -0.42 -0.03  0.00 -0.07  0.12  0.00  0.07  0.14  0.00 -0.04 0.84 0.162 2.9
thoughts        0.67  0.26  0.08  0.30  0.15 -0.09  0.14 -0.04  0.04 -0.09  0.12  0.05  0.07 0.70 0.304 2.3
reasoning       0.26  0.61  0.34 -0.01  0.12  0.00  0.13 -0.09 -0.12 -0.08  0.02 -0.02 -0.09 0.62 0.378 2.5
remembering     0.04  0.49  0.57 -0.16  0.03 -0.02 -0.09  0.07 -0.05 -0.01 -0.03  0.02 -0.09 0.62 0.384 2.3
beliefs         0.48  0.57 -0.15  0.14  0.05  0.19  0.08 -0.14 -0.02  0.04  0.00 -0.01  0.09 0.67 0.327 2.8
hungry          0.64 -0.47  0.06  0.50 -0.07  0.07  0.05 -0.08 -0.01  0.02 -0.08  0.00 -0.01 0.91 0.092 3.0
tired           0.74 -0.21  0.13  0.39 -0.02  0.04  0.03  0.08  0.04  0.14 -0.07 -0.14 -0.10 0.83 0.171 2.1
pain            0.70 -0.37  0.11  0.48 -0.05  0.10  0.04 -0.01 -0.02 -0.04  0.11 -0.02 -0.07 0.91 0.088 2.6
nauseated       0.98 -0.07  0.00 -0.15  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00 1.00 0.005 1.1
safe            0.72 -0.02  0.10  0.36  0.15 -0.03 -0.40 -0.05 -0.03 -0.04 -0.05 -0.02  0.02 0.85 0.151 2.3
love            0.70  0.46 -0.19  0.26 -0.07 -0.02 -0.02  0.03 -0.01  0.01  0.03  0.05 -0.02 0.81 0.186 2.3
recognizing     0.07  0.61  0.50 -0.23 -0.05 -0.01  0.01  0.00  0.10 -0.15 -0.09 -0.17  0.00 0.76 0.245 2.8
communicating   0.15  0.41  0.52 -0.01  0.00  0.08 -0.03  0.00  0.02 -0.17 -0.03 -0.16  0.02 0.53 0.470 2.7
guilt           0.62  0.47 -0.24  0.17 -0.07  0.15  0.02 -0.03  0.13  0.00 -0.06  0.08 -0.05 0.75 0.252 2.8
disrespected    0.64  0.45 -0.21  0.10 -0.08  0.28 -0.06  0.11  0.09  0.00 -0.15  0.01  0.05 0.80 0.202 3.0
free_will       0.69 -0.02  0.06  0.39  0.11 -0.05  0.01  0.06 -0.03 -0.06  0.00  0.17  0.08 0.69 0.307 1.9
choices         0.32  0.26  0.55  0.14  0.23  0.02  0.08  0.07 -0.24 -0.04 -0.16  0.17 -0.10 0.68 0.320 4.1
self_restraint  0.50  0.40  0.13  0.12  0.22  0.05  0.04 -0.13  0.23  0.05 -0.08  0.08 -0.07 0.59 0.406 3.6
intentions      0.58  0.12  0.21  0.32  0.25 -0.02  0.05  0.09  0.05  0.26 -0.04 -0.03  0.07 0.64 0.360 3.1
goal            0.21  0.33  0.52  0.04  0.04 -0.01  0.19  0.20 -0.02  0.06 -0.16 -0.02  0.05 0.54 0.463 3.1
conscious       0.72 -0.03  0.09  0.33  0.08  0.02  0.07 -0.05  0.10 -0.01  0.10  0.01 -0.01 0.67 0.331 1.6
self_aware      0.63  0.20  0.10  0.31  0.08 -0.01  0.15  0.04  0.14 -0.04  0.10  0.02  0.00 0.61 0.388 2.2
desires         0.74  0.00  0.06  0.34  0.18 -0.01  0.11  0.09 -0.03 -0.01  0.03 -0.03  0.16 0.74 0.263 1.8
embarrassed     0.59  0.55 -0.31  0.16 -0.08  0.27 -0.03 -0.01 -0.15  0.01  0.03 -0.04 -0.01 0.88 0.122 3.4
emo_recog       0.45  0.61  0.02  0.07  0.09  0.17  0.13 -0.19  0.00 -0.01  0.04 -0.05  0.09 0.68 0.321 2.5
joy             0.80  0.32 -0.11  0.25 -0.07 -0.35  0.02 -0.03 -0.03  0.00 -0.02 -0.03  0.01 0.94 0.055 2.1
morality        0.31  0.62  0.08  0.07  0.20  0.07 -0.06 -0.28  0.01  0.16  0.09  0.00 -0.09 0.65 0.346 2.6
personality     0.53  0.51  0.08  0.19  0.04  0.06  0.10  0.01  0.08 -0.03  0.02  0.03 -0.11 0.62 0.385 2.6
pleasure        0.77  0.00  0.06  0.37  0.04  0.01 -0.01  0.19 -0.02  0.01  0.06 -0.06  0.07 0.79 0.210 1.6
pride           0.67  0.51 -0.22  0.17 -0.04  0.13 -0.02 -0.01 -0.03 -0.03 -0.02 -0.03  0.06 0.81 0.185 2.4

                        MR1  MR2  MR4  MR3  MR6  MR5  MR7  MR8  MR9 MR11 MR10 MR12 MR13
SS loadings           13.54 5.47 3.85 2.61 0.70 0.49 0.41 0.40 0.31 0.27 0.23 0.22 0.18
Proportion Var         0.34 0.14 0.10 0.07 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00
Cumulative Var         0.34 0.48 0.57 0.64 0.65 0.67 0.68 0.69 0.69 0.70 0.71 0.71 0.72
Proportion Explained   0.47 0.19 0.13 0.09 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Proportion  0.47 0.66 0.80 0.89 0.91 0.93 0.94 0.96 0.97 0.98 0.99 0.99 1.00

Mean item complexity =  2.4
Test of the hypothesis that 13 factors are sufficient.

The degrees of freedom for the null model are  780  and the objective function was  36.39 with Chi Square of  14185
The degrees of freedom for the model are 338  and the objective function was  0.9 

The root mean square of the residuals (RMSR) is  0.01 
The df corrected root mean square of the residuals is  0.02 

The harmonic number of observations is  405 with the empirical chi square  67.74  with prob <  1 
The total number of observations was  405  with MLE Chi Square =  343.92  with prob <  0.4 

Tucker Lewis Index of factoring reliability =  0.999
RMSEA index =  0.014  and the 90 % confidence intervals are  NA 0.02
BIC =  -1685.39
Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                                MR1  MR2  MR4  MR3  MR6  MR5  MR7  MR8  MR9  MR11  MR10  MR12  MR13
Correlation of scores with factors             1.00 0.98 0.96 0.98 0.83 0.90 0.81 0.76 0.72  0.68  0.70  0.66  0.63
Multiple R square of scores with factors       1.00 0.96 0.92 0.95 0.69 0.81 0.65 0.58 0.51  0.46  0.50  0.44  0.40
Minimum correlation of possible factor scores  0.99 0.93 0.84 0.90 0.38 0.61 0.30 0.17 0.03 -0.07 -0.01 -0.12 -0.20
Factor Analysis using method =  minres
Call: fa(r = d1_all, nfactors = 13, rotate = "none", fm = "minres", 
    cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
                 MR1   MR2   MR4   MR3   MR6   MR5   MR7   MR8   MR9  MR11  MR10  MR12  MR13   h2    u2 com
happy           0.78  0.26 -0.08  0.29 -0.06 -0.20 -0.07  0.04  0.04  0.01  0.02  0.04 -0.06 0.82 0.179 1.8
depressed       0.75  0.38 -0.21  0.10 -0.11  0.01  0.03  0.10  0.04 -0.04 -0.04  0.09 -0.07 0.80 0.196 1.9
fear            0.74 -0.17  0.08  0.41  0.03  0.11 -0.12  0.09  0.02 -0.04  0.09 -0.01  0.00 0.80 0.202 1.9
angry           0.77  0.15 -0.05  0.23 -0.06  0.02 -0.01  0.16  0.08 -0.01  0.08  0.05  0.02 0.71 0.286 1.5
calm            0.74  0.03  0.11  0.25  0.11  0.00 -0.13  0.00  0.12 -0.01  0.03  0.05  0.00 0.67 0.325 1.5
sounds          0.11  0.16  0.59 -0.06 -0.30 -0.05 -0.14 -0.12  0.16  0.07  0.07  0.04  0.08 0.56 0.440 2.4
seeing          0.27  0.01  0.64  0.16 -0.25  0.09  0.04 -0.05 -0.04 -0.10  0.01  0.04  0.02 0.60 0.404 2.0
temperature     0.18  0.11  0.68  0.08 -0.25  0.06 -0.06 -0.03 -0.11  0.19  0.05  0.12  0.12 0.66 0.342 2.0
odors           0.37 -0.07  0.47  0.17 -0.24  0.06  0.01 -0.13  0.09 -0.06 -0.09  0.12  0.07 0.51 0.492 3.6
depth           0.22  0.25  0.53 -0.02 -0.11  0.01  0.04 -0.03 -0.03  0.09  0.04 -0.07  0.00 0.42 0.577 2.1
computations   -0.35  0.66  0.22 -0.42 -0.03  0.00 -0.07  0.12  0.00  0.07  0.14  0.00 -0.04 0.84 0.162 2.9
thoughts        0.67  0.26  0.08  0.30  0.15 -0.09  0.14 -0.04  0.04 -0.09  0.12  0.05  0.07 0.70 0.304 2.3
reasoning       0.26  0.61  0.34 -0.01  0.12  0.00  0.13 -0.09 -0.12 -0.08  0.02 -0.02 -0.09 0.62 0.378 2.5
remembering     0.04  0.49  0.57 -0.16  0.03 -0.02 -0.09  0.07 -0.05 -0.01 -0.03  0.02 -0.09 0.62 0.384 2.3
beliefs         0.48  0.57 -0.15  0.14  0.05  0.19  0.08 -0.14 -0.02  0.04  0.00 -0.01  0.09 0.67 0.327 2.8
hungry          0.64 -0.47  0.06  0.50 -0.07  0.07  0.05 -0.08 -0.01  0.02 -0.08  0.00 -0.01 0.91 0.092 3.0
tired           0.74 -0.21  0.13  0.39 -0.02  0.04  0.03  0.08  0.04  0.14 -0.07 -0.14 -0.10 0.83 0.171 2.1
pain            0.70 -0.37  0.11  0.48 -0.05  0.10  0.04 -0.01 -0.02 -0.04  0.11 -0.02 -0.07 0.91 0.088 2.6
nauseated       0.98 -0.07  0.00 -0.15  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00 1.00 0.005 1.1
safe            0.72 -0.02  0.10  0.36  0.15 -0.03 -0.40 -0.05 -0.03 -0.04 -0.05 -0.02  0.02 0.85 0.151 2.3
love            0.70  0.46 -0.19  0.26 -0.07 -0.02 -0.02  0.03 -0.01  0.01  0.03  0.05 -0.02 0.81 0.186 2.3
recognizing     0.07  0.61  0.50 -0.23 -0.05 -0.01  0.01  0.00  0.10 -0.15 -0.09 -0.17  0.00 0.76 0.245 2.8
communicating   0.15  0.41  0.52 -0.01  0.00  0.08 -0.03  0.00  0.02 -0.17 -0.03 -0.16  0.02 0.53 0.470 2.7
guilt           0.62  0.47 -0.24  0.17 -0.07  0.15  0.02 -0.03  0.13  0.00 -0.06  0.08 -0.05 0.75 0.252 2.8
disrespected    0.64  0.45 -0.21  0.10 -0.08  0.28 -0.06  0.11  0.09  0.00 -0.15  0.01  0.05 0.80 0.202 3.0
free_will       0.69 -0.02  0.06  0.39  0.11 -0.05  0.01  0.06 -0.03 -0.06  0.00  0.17  0.08 0.69 0.307 1.9
choices         0.32  0.26  0.55  0.14  0.23  0.02  0.08  0.07 -0.24 -0.04 -0.16  0.17 -0.10 0.68 0.320 4.1
self_restraint  0.50  0.40  0.13  0.12  0.22  0.05  0.04 -0.13  0.23  0.05 -0.08  0.08 -0.07 0.59 0.406 3.6
intentions      0.58  0.12  0.21  0.32  0.25 -0.02  0.05  0.09  0.05  0.26 -0.04 -0.03  0.07 0.64 0.360 3.1
goal            0.21  0.33  0.52  0.04  0.04 -0.01  0.19  0.20 -0.02  0.06 -0.16 -0.02  0.05 0.54 0.463 3.1
conscious       0.72 -0.03  0.09  0.33  0.08  0.02  0.07 -0.05  0.10 -0.01  0.10  0.01 -0.01 0.67 0.331 1.6
self_aware      0.63  0.20  0.10  0.31  0.08 -0.01  0.15  0.04  0.14 -0.04  0.10  0.02  0.00 0.61 0.388 2.2
desires         0.74  0.00  0.06  0.34  0.18 -0.01  0.11  0.09 -0.03 -0.01  0.03 -0.03  0.16 0.74 0.263 1.8
embarrassed     0.59  0.55 -0.31  0.16 -0.08  0.27 -0.03 -0.01 -0.15  0.01  0.03 -0.04 -0.01 0.88 0.122 3.4
emo_recog       0.45  0.61  0.02  0.07  0.09  0.17  0.13 -0.19  0.00 -0.01  0.04 -0.05  0.09 0.68 0.321 2.5
joy             0.80  0.32 -0.11  0.25 -0.07 -0.35  0.02 -0.03 -0.03  0.00 -0.02 -0.03  0.01 0.94 0.055 2.1
morality        0.31  0.62  0.08  0.07  0.20  0.07 -0.06 -0.28  0.01  0.16  0.09  0.00 -0.09 0.65 0.346 2.6
personality     0.53  0.51  0.08  0.19  0.04  0.06  0.10  0.01  0.08 -0.03  0.02  0.03 -0.11 0.62 0.385 2.6
pleasure        0.77  0.00  0.06  0.37  0.04  0.01 -0.01  0.19 -0.02  0.01  0.06 -0.06  0.07 0.79 0.210 1.6
pride           0.67  0.51 -0.22  0.17 -0.04  0.13 -0.02 -0.01 -0.03 -0.03 -0.02 -0.03  0.06 0.81 0.185 2.4

                        MR1  MR2  MR4  MR3  MR6  MR5  MR7  MR8  MR9 MR11 MR10 MR12 MR13
SS loadings           13.54 5.47 3.85 2.61 0.70 0.49 0.41 0.40 0.31 0.27 0.23 0.22 0.18
Proportion Var         0.34 0.14 0.10 0.07 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00
Cumulative Var         0.34 0.48 0.57 0.64 0.65 0.67 0.68 0.69 0.69 0.70 0.71 0.71 0.72
Proportion Explained   0.47 0.19 0.13 0.09 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Proportion  0.47 0.66 0.80 0.89 0.91 0.93 0.94 0.96 0.97 0.98 0.99 0.99 1.00

Mean item complexity =  2.4
Test of the hypothesis that 13 factors are sufficient.

The degrees of freedom for the null model are  780  and the objective function was  36.39 with Chi Square of  14185
The degrees of freedom for the model are 338  and the objective function was  0.9 

The root mean square of the residuals (RMSR) is  0.01 
The df corrected root mean square of the residuals is  0.02 

The harmonic number of observations is  405 with the empirical chi square  67.74  with prob <  1 
The total number of observations was  405  with MLE Chi Square =  343.92  with prob <  0.4 

Tucker Lewis Index of factoring reliability =  0.999
RMSEA index =  0.014  and the 90 % confidence intervals are  NA 0.02
BIC =  -1685.39
Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                                MR1  MR2  MR4  MR3  MR6  MR5  MR7  MR8  MR9  MR11  MR10  MR12  MR13
Correlation of scores with factors             1.00 0.98 0.96 0.98 0.83 0.90 0.81 0.76 0.72  0.68  0.70  0.66  0.63
Multiple R square of scores with factors       1.00 0.96 0.92 0.95 0.69 0.81 0.65 0.58 0.51  0.46  0.50  0.44  0.40
Minimum correlation of possible factor scores  0.99 0.93 0.84 0.90 0.38 0.61 0.30 0.17 0.03 -0.07 -0.01 -0.12 -0.20
[1] 4
[1] 3

Step 2: Run EFA with varimax rotation

Factor Analysis using method =  minres
Call: fa(r = d1_all, nfactors = nfactors_d1_all, rotate = chosenRotType, 
    fm = "minres", cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
                 MR1   MR2   MR3   h2   u2 com
happy           0.55  0.65  0.10 0.74 0.26 2.0
depressed       0.37  0.78  0.03 0.75 0.25 1.4
fear            0.82  0.28  0.06 0.76 0.24 1.2
angry           0.58  0.57  0.08 0.67 0.33 2.0
calm            0.65  0.41  0.17 0.62 0.38 1.9
sounds          0.06 -0.05  0.61 0.38 0.62 1.0
seeing          0.36 -0.07  0.61 0.51 0.49 1.7
temperature     0.21 -0.06  0.66 0.48 0.52 1.2
odors           0.45 -0.01  0.43 0.39 0.61 2.0
depth           0.11  0.11  0.62 0.40 0.60 1.1
computations   -0.74  0.19  0.44 0.78 0.22 1.8
thoughts        0.50  0.55  0.22 0.61 0.39 2.3
reasoning      -0.06  0.47  0.57 0.55 0.45 2.0
remembering    -0.20  0.17  0.72 0.59 0.41 1.3
beliefs         0.11  0.76  0.12 0.60 0.40 1.1
hungry          0.93  0.01 -0.08 0.87 0.13 1.0
tired           0.83  0.23  0.10 0.76 0.24 1.2
pain            0.93  0.10  0.01 0.87 0.13 1.0
nauseated       0.65  0.50  0.09 0.68 0.32 1.9
safe            0.70  0.36  0.13 0.63 0.37 1.6
love            0.37  0.81  0.06 0.79 0.21 1.4
recognizing    -0.29  0.29  0.71 0.67 0.33 1.7
communicating  -0.02  0.20  0.65 0.46 0.54 1.2
guilt           0.26  0.80  0.02 0.70 0.30 1.2
disrespected    0.25  0.78  0.04 0.68 0.32 1.2
free_will       0.70  0.37  0.09 0.63 0.37 1.5
choices         0.25  0.18  0.60 0.46 0.54 1.5
self_restraint  0.24  0.55  0.31 0.45 0.55 2.0
intentions      0.54  0.35  0.27 0.48 0.52 2.3
goal            0.09  0.17  0.62 0.42 0.58 1.2
conscious       0.70  0.36  0.12 0.64 0.36 1.6
self_aware      0.52  0.48  0.22 0.55 0.45 2.3
desires         0.69  0.40  0.11 0.66 0.34 1.7
embarrassed     0.19  0.85 -0.01 0.76 0.24 1.1
emo_recog       0.06  0.70  0.29 0.58 0.42 1.3
joy             0.51  0.70  0.10 0.76 0.24 1.9
morality       -0.04  0.60  0.32 0.47 0.53 1.5
personality     0.23  0.66  0.31 0.58 0.42 1.7
pleasure        0.74  0.43  0.11 0.74 0.26 1.7
pride           0.28  0.85  0.05 0.80 0.20 1.2

                       MR1  MR2  MR3
SS loadings           9.92 9.81 5.17
Proportion Var        0.25 0.25 0.13
Cumulative Var        0.25 0.49 0.62
Proportion Explained  0.40 0.39 0.21
Cumulative Proportion 0.40 0.79 1.00

Mean item complexity =  1.6
Test of the hypothesis that 3 factors are sufficient.

The degrees of freedom for the null model are  780  and the objective function was  36.39 with Chi Square of  14185
The degrees of freedom for the model are 663  and the objective function was  3.81 

The root mean square of the residuals (RMSR) is  0.03 
The df corrected root mean square of the residuals is  0.03 

The harmonic number of observations is  405 with the empirical chi square  500.89  with prob <  1 
The total number of observations was  405  with MLE Chi Square =  1475.92  with prob <  8.7e-64 

Tucker Lewis Index of factoring reliability =  0.928
RMSEA index =  0.057  and the 90 % confidence intervals are  0.051 0.059
BIC =  -2504.66
Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                                MR1  MR2  MR3
Correlation of scores with factors             0.98 0.98 0.95
Multiple R square of scores with factors       0.97 0.95 0.91
Minimum correlation of possible factor scores  0.93 0.91 0.81

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Factor loadings table

Study 2

Design: 2 conditions (beetle, robot), between-subjects (replication of Study 1) Date conducted: 2016-01-12

Demographics

Joining, by = c("condition", "min_age", "max_age", "median_age", "mean_age", "sd_age")
joining character vector and factor, coercing into character vector

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Exploratory factor analysis

Step 1: Run maximal EFA (without and with rotation)

Parallel analysis suggests that the number of factors =  3  and the number of components =  3 

Factor Analysis using method =  minres
Call: fa(r = d2_all, nfactors = 13, rotate = "none", fm = "minres", 
    cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
                 MR1   MR2   MR3   MR4   MR5   MR6   MR7  MR11   MR9   MR8  MR10  MR12  MR13   h2    u2 com
happy           0.84  0.15 -0.09 -0.22  0.04 -0.07 -0.02  0.04 -0.03  0.10 -0.01  0.06 -0.10 0.82 0.179 1.3
depressed       0.79  0.31 -0.27 -0.09 -0.32  0.11 -0.01  0.00 -0.05 -0.01 -0.01  0.01  0.01 0.93 0.072 2.0
fear            0.80 -0.32  0.09  0.00 -0.03 -0.02 -0.06 -0.01 -0.03  0.05  0.15  0.09  0.12 0.80 0.203 1.5
angry           0.78 -0.01 -0.04 -0.07  0.02 -0.14  0.02 -0.01 -0.09  0.01  0.15 -0.06  0.11 0.68 0.320 1.3
calm            0.73  0.01  0.16 -0.21  0.17 -0.04 -0.01  0.05  0.09  0.06 -0.06  0.11  0.01 0.66 0.339 1.5
sounds          0.12  0.17  0.68 -0.07 -0.10 -0.09  0.13 -0.01  0.03  0.12  0.12  0.01 -0.02 0.59 0.414 1.6
seeing          0.37 -0.05  0.65  0.14 -0.07 -0.06  0.00 -0.14 -0.05  0.06 -0.10  0.08 -0.07 0.63 0.367 2.1
temperature     0.25  0.00  0.61  0.09 -0.24 -0.15 -0.09  0.15  0.13  0.00  0.02 -0.05 -0.08 0.59 0.412 2.3
odors           0.44 -0.13  0.52  0.20 -0.25 -0.11 -0.23  0.17  0.09  0.01 -0.03 -0.09  0.05 0.69 0.307 4.1
depth           0.16  0.23  0.51  0.10 -0.10 -0.11 -0.09  0.14  0.00 -0.07  0.08 -0.04  0.06 0.41 0.585 2.4
computations   -0.39  0.75  0.28 -0.09 -0.05 -0.05  0.08  0.02  0.06 -0.03  0.09  0.00  0.01 0.83 0.175 2.0
thoughts        0.75  0.10  0.11 -0.04  0.17  0.27  0.01  0.10  0.04 -0.02 -0.04 -0.04  0.00 0.71 0.295 1.5
reasoning       0.24  0.55  0.34  0.11  0.09  0.16 -0.11  0.10 -0.02 -0.10 -0.02  0.06 -0.08 0.56 0.437 3.0
remembering     0.06  0.48  0.58 -0.07  0.01  0.01  0.10 -0.11  0.10  0.06 -0.06  0.06 -0.07 0.63 0.369 2.3
beliefs         0.49  0.43 -0.07  0.18 -0.02  0.08  0.17  0.14  0.00  0.02 -0.12  0.20  0.00 0.58 0.419 3.4
hungry          0.69 -0.64  0.03  0.16 -0.06 -0.05  0.00 -0.02  0.02  0.01 -0.06  0.01 -0.06 0.92 0.081 2.2
tired           0.77 -0.39  0.11 -0.01  0.01 -0.03  0.07 -0.04 -0.07 -0.03  0.05  0.03  0.10 0.79 0.211 1.6
pain            0.74 -0.52  0.07  0.08 -0.03 -0.02  0.02 -0.05  0.01 -0.07  0.07  0.02 -0.03 0.84 0.157 1.9
nauseated       0.73 -0.08 -0.03 -0.02 -0.01 -0.08  0.03 -0.01  0.00 -0.07  0.06  0.11  0.02 0.57 0.431 1.1
safe            0.76 -0.17  0.11 -0.04  0.16 -0.04 -0.07  0.10 -0.06  0.16 -0.05  0.09  0.08 0.71 0.291 1.5
love            0.75  0.27 -0.20 -0.01 -0.02  0.02  0.12 -0.01  0.11 -0.05  0.02 -0.02 -0.12 0.71 0.287 1.6
recognizing     0.07  0.63  0.44 -0.04  0.00 -0.07  0.01 -0.03  0.03  0.04  0.06  0.07  0.01 0.61 0.391 1.9
communicating   0.25  0.28  0.62  0.02 -0.15 -0.03  0.15 -0.19  0.00 -0.03 -0.01  0.01  0.09 0.62 0.378 2.4
guilt           0.64  0.47 -0.32  0.22  0.04 -0.12  0.02 -0.06  0.10  0.02 -0.10 -0.03  0.13 0.84 0.157 3.1
disrespected    0.63  0.36 -0.24  0.20  0.06 -0.08 -0.05  0.01 -0.05  0.09  0.19 -0.10 -0.07 0.70 0.299 2.7
free_will       0.68 -0.27  0.14  0.05 -0.01  0.18  0.11 -0.13  0.03  0.02  0.05 -0.13 -0.06 0.65 0.348 1.9
choices         0.37  0.22  0.59 -0.01  0.04  0.08  0.07 -0.08 -0.07 -0.06 -0.02 -0.01 -0.06 0.56 0.442 2.3
self_restraint  0.42  0.40  0.14  0.17  0.15  0.13 -0.06  0.02 -0.01 -0.04 -0.08 -0.05  0.07 0.45 0.554 3.3
intentions      0.63 -0.06  0.31  0.10  0.12  0.16  0.23  0.08 -0.19  0.03 -0.07 -0.15  0.06 0.68 0.316 2.6
goal            0.30  0.23  0.59  0.00 -0.04 -0.02  0.05  0.08 -0.15  0.03 -0.16 -0.11  0.08 0.57 0.425 2.4
conscious       0.74 -0.25  0.15 -0.04  0.11  0.27 -0.04  0.02  0.11  0.13  0.02  0.05  0.04 0.76 0.245 1.9
self_aware      0.70  0.15  0.12 -0.02  0.10  0.27 -0.17 -0.08  0.16  0.08  0.11 -0.11  0.01 0.70 0.304 2.0
desires         0.80 -0.06  0.06 -0.02  0.10 -0.01  0.15  0.15 -0.07 -0.18  0.07  0.00 -0.07 0.74 0.256 1.4
embarrassed     0.61  0.44 -0.31  0.22  0.01 -0.08  0.10  0.02  0.12  0.04 -0.06 -0.02  0.00 0.74 0.257 3.0
emo_recog       0.50  0.54  0.07  0.20  0.13 -0.01 -0.18 -0.06 -0.11 -0.15  0.02  0.12 -0.02 0.69 0.312 3.1
joy             0.84  0.13 -0.05 -0.28  0.09 -0.17 -0.08 -0.02 -0.05  0.06 -0.07 -0.09 -0.05 0.87 0.134 1.5
morality        0.31  0.59  0.11  0.26  0.14  0.07 -0.27 -0.09 -0.13 -0.12 -0.01  0.01 -0.05 0.65 0.345 3.1
personality     0.58  0.43  0.11 -0.11  0.07  0.00 -0.08 -0.14 -0.07 -0.01 -0.05 -0.02 -0.03 0.59 0.414 2.3
pleasure        0.84 -0.11  0.02 -0.23  0.10 -0.05  0.00  0.00  0.13 -0.22 -0.03 -0.02  0.06 0.85 0.150 1.4
pride           0.69  0.39 -0.25  0.24  0.12 -0.07  0.11  0.03  0.00  0.06  0.12  0.00 -0.04 0.80 0.201 2.5

                        MR1  MR2  MR3  MR4  MR5  MR6  MR7 MR11  MR9  MR8 MR10 MR12 MR13
SS loadings           14.61 4.99 4.33 0.77 0.53 0.52 0.47 0.30 0.27 0.26 0.26 0.23 0.17
Proportion Var         0.37 0.12 0.11 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00
Cumulative Var         0.37 0.49 0.60 0.62 0.63 0.64 0.66 0.66 0.67 0.68 0.68 0.69 0.69
Proportion Explained   0.53 0.18 0.16 0.03 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Proportion  0.53 0.71 0.86 0.89 0.91 0.93 0.95 0.96 0.97 0.98 0.99 0.99 1.00

Mean item complexity =  2.2
Test of the hypothesis that 13 factors are sufficient.

The degrees of freedom for the null model are  780  and the objective function was  33.07 with Chi Square of  12924.08
The degrees of freedom for the model are 338  and the objective function was  0.92 

The root mean square of the residuals (RMSR) is  0.01 
The df corrected root mean square of the residuals is  0.02 

The harmonic number of observations is  406 with the empirical chi square  77.28  with prob <  1 
The total number of observations was  406  with MLE Chi Square =  351.09  with prob <  0.3 

Tucker Lewis Index of factoring reliability =  0.997
RMSEA index =  0.016  and the 90 % confidence intervals are  NA 0.021
BIC =  -1679.06
Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                                MR1  MR2  MR3  MR4  MR5  MR6  MR7  MR11  MR9  MR8  MR10  MR12  MR13
Correlation of scores with factors             0.99 0.98 0.96 0.89 0.86 0.82 0.77  0.68 0.71 0.72  0.70  0.65  0.64
Multiple R square of scores with factors       0.99 0.96 0.93 0.78 0.74 0.67 0.60  0.47 0.50 0.52  0.49  0.42  0.41
Minimum correlation of possible factor scores  0.97 0.92 0.86 0.57 0.48 0.33 0.20 -0.06 0.00 0.03 -0.02 -0.15 -0.18
Factor Analysis using method =  minres
Call: fa(r = d2_all, nfactors = 13, rotate = "none", fm = "minres", 
    cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
                 MR1   MR2   MR3   MR4   MR5   MR6   MR7  MR11   MR9   MR8  MR10  MR12  MR13   h2    u2 com
happy           0.84  0.15 -0.09 -0.22  0.04 -0.07 -0.02  0.04 -0.03  0.10 -0.01  0.06 -0.10 0.82 0.179 1.3
depressed       0.79  0.31 -0.27 -0.09 -0.32  0.11 -0.01  0.00 -0.05 -0.01 -0.01  0.01  0.01 0.93 0.072 2.0
fear            0.80 -0.32  0.09  0.00 -0.03 -0.02 -0.06 -0.01 -0.03  0.05  0.15  0.09  0.12 0.80 0.203 1.5
angry           0.78 -0.01 -0.04 -0.07  0.02 -0.14  0.02 -0.01 -0.09  0.01  0.15 -0.06  0.11 0.68 0.320 1.3
calm            0.73  0.01  0.16 -0.21  0.17 -0.04 -0.01  0.05  0.09  0.06 -0.06  0.11  0.01 0.66 0.339 1.5
sounds          0.12  0.17  0.68 -0.07 -0.10 -0.09  0.13 -0.01  0.03  0.12  0.12  0.01 -0.02 0.59 0.414 1.6
seeing          0.37 -0.05  0.65  0.14 -0.07 -0.06  0.00 -0.14 -0.05  0.06 -0.10  0.08 -0.07 0.63 0.367 2.1
temperature     0.25  0.00  0.61  0.09 -0.24 -0.15 -0.09  0.15  0.13  0.00  0.02 -0.05 -0.08 0.59 0.412 2.3
odors           0.44 -0.13  0.52  0.20 -0.25 -0.11 -0.23  0.17  0.09  0.01 -0.03 -0.09  0.05 0.69 0.307 4.1
depth           0.16  0.23  0.51  0.10 -0.10 -0.11 -0.09  0.14  0.00 -0.07  0.08 -0.04  0.06 0.41 0.585 2.4
computations   -0.39  0.75  0.28 -0.09 -0.05 -0.05  0.08  0.02  0.06 -0.03  0.09  0.00  0.01 0.83 0.175 2.0
thoughts        0.75  0.10  0.11 -0.04  0.17  0.27  0.01  0.10  0.04 -0.02 -0.04 -0.04  0.00 0.71 0.295 1.5
reasoning       0.24  0.55  0.34  0.11  0.09  0.16 -0.11  0.10 -0.02 -0.10 -0.02  0.06 -0.08 0.56 0.437 3.0
remembering     0.06  0.48  0.58 -0.07  0.01  0.01  0.10 -0.11  0.10  0.06 -0.06  0.06 -0.07 0.63 0.369 2.3
beliefs         0.49  0.43 -0.07  0.18 -0.02  0.08  0.17  0.14  0.00  0.02 -0.12  0.20  0.00 0.58 0.419 3.4
hungry          0.69 -0.64  0.03  0.16 -0.06 -0.05  0.00 -0.02  0.02  0.01 -0.06  0.01 -0.06 0.92 0.081 2.2
tired           0.77 -0.39  0.11 -0.01  0.01 -0.03  0.07 -0.04 -0.07 -0.03  0.05  0.03  0.10 0.79 0.211 1.6
pain            0.74 -0.52  0.07  0.08 -0.03 -0.02  0.02 -0.05  0.01 -0.07  0.07  0.02 -0.03 0.84 0.157 1.9
nauseated       0.73 -0.08 -0.03 -0.02 -0.01 -0.08  0.03 -0.01  0.00 -0.07  0.06  0.11  0.02 0.57 0.431 1.1
safe            0.76 -0.17  0.11 -0.04  0.16 -0.04 -0.07  0.10 -0.06  0.16 -0.05  0.09  0.08 0.71 0.291 1.5
love            0.75  0.27 -0.20 -0.01 -0.02  0.02  0.12 -0.01  0.11 -0.05  0.02 -0.02 -0.12 0.71 0.287 1.6
recognizing     0.07  0.63  0.44 -0.04  0.00 -0.07  0.01 -0.03  0.03  0.04  0.06  0.07  0.01 0.61 0.391 1.9
communicating   0.25  0.28  0.62  0.02 -0.15 -0.03  0.15 -0.19  0.00 -0.03 -0.01  0.01  0.09 0.62 0.378 2.4
guilt           0.64  0.47 -0.32  0.22  0.04 -0.12  0.02 -0.06  0.10  0.02 -0.10 -0.03  0.13 0.84 0.157 3.1
disrespected    0.63  0.36 -0.24  0.20  0.06 -0.08 -0.05  0.01 -0.05  0.09  0.19 -0.10 -0.07 0.70 0.299 2.7
free_will       0.68 -0.27  0.14  0.05 -0.01  0.18  0.11 -0.13  0.03  0.02  0.05 -0.13 -0.06 0.65 0.348 1.9
choices         0.37  0.22  0.59 -0.01  0.04  0.08  0.07 -0.08 -0.07 -0.06 -0.02 -0.01 -0.06 0.56 0.442 2.3
self_restraint  0.42  0.40  0.14  0.17  0.15  0.13 -0.06  0.02 -0.01 -0.04 -0.08 -0.05  0.07 0.45 0.554 3.3
intentions      0.63 -0.06  0.31  0.10  0.12  0.16  0.23  0.08 -0.19  0.03 -0.07 -0.15  0.06 0.68 0.316 2.6
goal            0.30  0.23  0.59  0.00 -0.04 -0.02  0.05  0.08 -0.15  0.03 -0.16 -0.11  0.08 0.57 0.425 2.4
conscious       0.74 -0.25  0.15 -0.04  0.11  0.27 -0.04  0.02  0.11  0.13  0.02  0.05  0.04 0.76 0.245 1.9
self_aware      0.70  0.15  0.12 -0.02  0.10  0.27 -0.17 -0.08  0.16  0.08  0.11 -0.11  0.01 0.70 0.304 2.0
desires         0.80 -0.06  0.06 -0.02  0.10 -0.01  0.15  0.15 -0.07 -0.18  0.07  0.00 -0.07 0.74 0.256 1.4
embarrassed     0.61  0.44 -0.31  0.22  0.01 -0.08  0.10  0.02  0.12  0.04 -0.06 -0.02  0.00 0.74 0.257 3.0
emo_recog       0.50  0.54  0.07  0.20  0.13 -0.01 -0.18 -0.06 -0.11 -0.15  0.02  0.12 -0.02 0.69 0.312 3.1
joy             0.84  0.13 -0.05 -0.28  0.09 -0.17 -0.08 -0.02 -0.05  0.06 -0.07 -0.09 -0.05 0.87 0.134 1.5
morality        0.31  0.59  0.11  0.26  0.14  0.07 -0.27 -0.09 -0.13 -0.12 -0.01  0.01 -0.05 0.65 0.345 3.1
personality     0.58  0.43  0.11 -0.11  0.07  0.00 -0.08 -0.14 -0.07 -0.01 -0.05 -0.02 -0.03 0.59 0.414 2.3
pleasure        0.84 -0.11  0.02 -0.23  0.10 -0.05  0.00  0.00  0.13 -0.22 -0.03 -0.02  0.06 0.85 0.150 1.4
pride           0.69  0.39 -0.25  0.24  0.12 -0.07  0.11  0.03  0.00  0.06  0.12  0.00 -0.04 0.80 0.201 2.5

                        MR1  MR2  MR3  MR4  MR5  MR6  MR7 MR11  MR9  MR8 MR10 MR12 MR13
SS loadings           14.61 4.99 4.33 0.77 0.53 0.52 0.47 0.30 0.27 0.26 0.26 0.23 0.17
Proportion Var         0.37 0.12 0.11 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00
Cumulative Var         0.37 0.49 0.60 0.62 0.63 0.64 0.66 0.66 0.67 0.68 0.68 0.69 0.69
Proportion Explained   0.53 0.18 0.16 0.03 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Proportion  0.53 0.71 0.86 0.89 0.91 0.93 0.95 0.96 0.97 0.98 0.99 0.99 1.00

Mean item complexity =  2.2
Test of the hypothesis that 13 factors are sufficient.

The degrees of freedom for the null model are  780  and the objective function was  33.07 with Chi Square of  12924.08
The degrees of freedom for the model are 338  and the objective function was  0.92 

The root mean square of the residuals (RMSR) is  0.01 
The df corrected root mean square of the residuals is  0.02 

The harmonic number of observations is  406 with the empirical chi square  77.28  with prob <  1 
The total number of observations was  406  with MLE Chi Square =  351.09  with prob <  0.3 

Tucker Lewis Index of factoring reliability =  0.997
RMSEA index =  0.016  and the 90 % confidence intervals are  NA 0.021
BIC =  -1679.06
Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                                MR1  MR2  MR3  MR4  MR5  MR6  MR7  MR11  MR9  MR8  MR10  MR12  MR13
Correlation of scores with factors             0.99 0.98 0.96 0.89 0.86 0.82 0.77  0.68 0.71 0.72  0.70  0.65  0.64
Multiple R square of scores with factors       0.99 0.96 0.93 0.78 0.74 0.67 0.60  0.47 0.50 0.52  0.49  0.42  0.41
Minimum correlation of possible factor scores  0.97 0.92 0.86 0.57 0.48 0.33 0.20 -0.06 0.00 0.03 -0.02 -0.15 -0.18
[1] 3
[1] 3

Step 2: Run EFA with varimax rotation

Factor Analysis using method =  minres
Call: fa(r = d2_all, nfactors = nfactors_d2_all, rotate = chosenRotType, 
    fm = "minres", cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
                 MR1   MR2   MR3   h2   u2 com
happy           0.55  0.64  0.09 0.72 0.28 2.0
depressed       0.39  0.75  0.00 0.71 0.29 1.5
fear            0.83  0.23  0.09 0.75 0.25 1.2
angry           0.60  0.49  0.08 0.61 0.39 2.0
calm            0.57  0.41  0.25 0.55 0.45 2.2
sounds          0.07 -0.05  0.72 0.52 0.48 1.0
seeing          0.39 -0.03  0.63 0.56 0.44 1.7
temperature     0.26 -0.07  0.60 0.43 0.57 1.4
odors           0.47 -0.01  0.48 0.45 0.55 2.0
depth           0.04  0.08  0.58 0.34 0.66 1.0
computations   -0.73  0.18  0.47 0.79 0.21 1.8
thoughts        0.53  0.50  0.23 0.58 0.42 2.4
reasoning      -0.11  0.42  0.52 0.47 0.53 2.0
remembering    -0.19  0.17  0.72 0.58 0.42 1.3
beliefs         0.11  0.64  0.15 0.44 0.56 1.2
hungry          0.92 -0.02 -0.10 0.86 0.14 1.0
tired           0.86  0.16  0.08 0.77 0.23 1.1
pain            0.90  0.07 -0.01 0.82 0.18 1.0
nauseated       0.61  0.40  0.06 0.54 0.46 1.7
safe            0.70  0.32  0.14 0.62 0.38 1.5
love            0.39  0.72  0.02 0.66 0.34 1.5
recognizing    -0.29  0.33  0.63 0.59 0.41 2.0
communicating   0.09  0.12  0.72 0.54 0.46 1.1
guilt           0.17  0.84 -0.04 0.73 0.27 1.1
disrespected    0.24  0.74 -0.01 0.60 0.40 1.2
free_will       0.71  0.18  0.14 0.56 0.44 1.2
choices         0.21  0.17  0.67 0.53 0.47 1.3
self_restraint  0.09  0.51  0.31 0.37 0.63 1.7
intentions      0.56  0.25  0.34 0.50 0.50 2.1
goal            0.16  0.13  0.67 0.49 0.51 1.2
conscious       0.74  0.23  0.15 0.62 0.38 1.3
self_aware      0.46  0.49  0.25 0.52 0.48 2.5
desires         0.66  0.43  0.14 0.64 0.36 1.8
embarrassed     0.16  0.80 -0.05 0.67 0.33 1.1
emo_recog       0.07  0.67  0.31 0.55 0.45 1.4
joy             0.56  0.61  0.12 0.69 0.31 2.1
morality       -0.11  0.57  0.33 0.45 0.55 1.7
personality     0.20  0.61  0.33 0.53 0.47 1.8
pleasure        0.71  0.43  0.10 0.70 0.30 1.7
pride           0.26  0.80 -0.01 0.71 0.29 1.2

                       MR1  MR2  MR3
SS loadings           9.82 8.40 5.55
Proportion Var        0.25 0.21 0.14
Cumulative Var        0.25 0.46 0.59
Proportion Explained  0.41 0.35 0.23
Cumulative Proportion 0.41 0.77 1.00

Mean item complexity =  1.6
Test of the hypothesis that 3 factors are sufficient.

The degrees of freedom for the null model are  780  and the objective function was  33.07 with Chi Square of  12924.08
The degrees of freedom for the model are 663  and the objective function was  3.93 

The root mean square of the residuals (RMSR) is  0.03 
The df corrected root mean square of the residuals is  0.03 

The harmonic number of observations is  406 with the empirical chi square  607.3  with prob <  0.94 
The total number of observations was  406  with MLE Chi Square =  1526.24  with prob <  6.5e-70 

Tucker Lewis Index of factoring reliability =  0.916
RMSEA index =  0.059  and the 90 % confidence intervals are  0.053 0.06
BIC =  -2455.97
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy             
                                                MR1  MR2  MR3
Correlation of scores with factors             0.98 0.97 0.96
Multiple R square of scores with factors       0.96 0.94 0.92
Minimum correlation of possible factor scores  0.93 0.89 0.83

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Factor loadings table

Study 3

Design: 2 conditions (beetle, robot), within-subjects Date conducted: 2016-01-10

Demographics

NAs introduced by coercion

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Exploratory factor analysis

Step 1: Run maximal EFA (without and with rotation)

Parallel analysis suggests that the number of factors =  3  and the number of components =  3 

Factor Analysis using method =  minres
Call: fa(r = d3_all, nfactors = 13, rotate = "none", fm = "minres", 
    cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
                 MR1   MR2   MR3   MR4   MR5   MR7   MR6   MR9   MR8  MR10  MR12  MR11  MR13   h2    u2 com
happy           0.63  0.62 -0.13 -0.05 -0.20  0.01 -0.24 -0.07 -0.07  0.00  0.02 -0.01  0.00 0.91 0.088 2.6
depressed       0.50  0.60 -0.24  0.16 -0.10  0.00  0.12  0.08 -0.01  0.05 -0.04  0.05 -0.01 0.74 0.262 2.7
fear            0.80  0.27 -0.02 -0.19 -0.04 -0.03  0.03  0.00  0.02  0.04  0.03  0.06  0.02 0.76 0.236 1.4
angry           0.62  0.53 -0.14 -0.12 -0.09 -0.01  0.06 -0.02  0.03  0.02  0.01 -0.07  0.06 0.72 0.275 2.3
calm            0.66  0.47  0.05 -0.14 -0.05 -0.07 -0.06  0.04  0.04  0.12 -0.05  0.12  0.04 0.72 0.282 2.2
sounds          0.18  0.24  0.61  0.00 -0.22  0.25 -0.04  0.07 -0.06 -0.08 -0.17 -0.04  0.03 0.63 0.371 2.6
seeing          0.35  0.15  0.54  0.00 -0.10  0.18 -0.05  0.07  0.01  0.02 -0.05 -0.01  0.02 0.49 0.514 2.4
temperature     0.38  0.15  0.59  0.00 -0.19  0.13  0.14 -0.08 -0.01 -0.14  0.09  0.07  0.03 0.63 0.375 2.7
odors           0.54  0.12  0.41  0.03 -0.07  0.29  0.04 -0.05  0.06 -0.01  0.12  0.04 -0.04 0.59 0.410 2.9
depth           0.23  0.30  0.50  0.08 -0.08  0.04  0.21  0.00 -0.01 -0.06 -0.08  0.10 -0.10 0.48 0.521 3.0
computations   -0.75  0.31  0.15  0.17 -0.03 -0.22  0.03 -0.01  0.02 -0.21  0.04  0.08  0.02 0.82 0.176 2.0
thoughts        0.58  0.57  0.10 -0.14  0.12 -0.04 -0.13  0.10  0.12 -0.03 -0.10 -0.03  0.00 0.76 0.239 2.6
reasoning      -0.02  0.61  0.35  0.17  0.08 -0.20 -0.07  0.11  0.05  0.02 -0.05  0.01 -0.06 0.59 0.405 2.3
remembering    -0.10  0.47  0.49  0.10 -0.07 -0.12  0.11 -0.04 -0.05  0.06 -0.02 -0.20  0.06 0.57 0.435 2.9
beliefs         0.25  0.59 -0.13  0.19  0.19  0.08  0.10 -0.25 -0.10 -0.07 -0.08  0.01  0.00 0.59 0.408 2.7
hungry          0.99 -0.15  0.01  0.03  0.00 -0.01  0.00  0.00  0.00  0.00  0.00  0.00  0.00 1.00 0.005 1.0
tired           0.85  0.17 -0.04 -0.12 -0.03  0.01  0.09  0.00 -0.06  0.04  0.08  0.16  0.03 0.81 0.187 1.3
pain            0.89  0.01  0.00 -0.15 -0.02  0.10  0.05  0.04  0.00  0.01  0.03  0.00 -0.04 0.83 0.167 1.1
nauseated       0.70  0.38 -0.12 -0.08 -0.12 -0.04  0.24  0.18 -0.06  0.01 -0.01  0.10 -0.05 0.78 0.222 2.2
safe            0.72  0.29  0.04 -0.13  0.01  0.03 -0.05  0.07  0.02  0.08  0.04  0.00  0.07 0.65 0.351 1.5
love            0.56  0.61 -0.14  0.07 -0.01 -0.03  0.08  0.15 -0.21 -0.03  0.02 -0.15 -0.07 0.81 0.190 2.7
recognizing    -0.19  0.57  0.45  0.18 -0.11 -0.06 -0.08  0.14  0.09  0.09 -0.02  0.00  0.01 0.65 0.352 2.9
communicating  -0.05  0.39  0.47  0.19 -0.03 -0.08  0.00 -0.02 -0.01  0.15  0.11 -0.04  0.04 0.45 0.548 2.8
guilt           0.36  0.64 -0.24  0.22  0.01  0.10  0.05  0.01  0.06 -0.04 -0.03 -0.04  0.01 0.66 0.335 2.3
disrespected    0.37  0.63 -0.23  0.21  0.04  0.14  0.18 -0.06  0.03  0.05 -0.13 -0.06  0.09 0.72 0.277 2.8
free_will       0.73  0.35  0.05 -0.23  0.25  0.05 -0.08  0.05 -0.18 -0.14 -0.02  0.02  0.07 0.84 0.159 2.3
choices         0.31  0.41  0.46 -0.05  0.13 -0.12 -0.05 -0.06 -0.01  0.06  0.02  0.04  0.09 0.53 0.468 3.4
self_restraint  0.29  0.58  0.02  0.03  0.14 -0.11  0.06 -0.07 -0.15  0.15 -0.11  0.05  0.02 0.53 0.474 2.2
intentions      0.57  0.37  0.22 -0.20  0.22 -0.10  0.08 -0.05  0.07  0.00  0.04 -0.13  0.03 0.64 0.357 3.2
goal            0.20  0.41  0.43  0.02 -0.04 -0.09  0.13 -0.13 -0.11  0.09  0.12 -0.11  0.05 0.48 0.517 3.7
conscious       0.73  0.36  0.05 -0.19  0.15  0.03 -0.02  0.01  0.09 -0.02 -0.05  0.01 -0.16 0.76 0.239 1.9
self_aware      0.54  0.48  0.06 -0.15  0.18  0.04 -0.06  0.04  0.15  0.02 -0.06  0.09  0.10 0.64 0.362 2.8
desires         0.75  0.39  0.02 -0.28  0.06 -0.01  0.15 -0.10  0.08 -0.04  0.06 -0.06 -0.05 0.84 0.156 2.1
embarrassed     0.35  0.60 -0.28  0.25  0.03  0.10  0.00  0.07  0.21 -0.15  0.06 -0.04  0.08 0.73 0.274 3.3
emo_recog       0.24  0.66  0.07  0.21  0.26  0.09 -0.13  0.02 -0.01  0.09  0.16 -0.01 -0.15 0.69 0.310 2.4
joy             0.62  0.61 -0.15 -0.02 -0.20 -0.07 -0.02 -0.06  0.06 -0.07 -0.03  0.02 -0.08 0.84 0.162 2.5
morality        0.05  0.58  0.14  0.35  0.26  0.04 -0.06 -0.19 -0.07  0.05 -0.08  0.15 -0.11 0.63 0.366 3.0
personality     0.33  0.67  0.09  0.05  0.10  0.01 -0.07  0.11  0.07  0.01  0.12 -0.05 -0.01 0.61 0.386 1.8
pleasure        0.77  0.40 -0.07 -0.12 -0.08 -0.06  0.09 -0.11  0.05  0.05 -0.06 -0.02  0.02 0.81 0.194 1.8
pride           0.40  0.65 -0.24  0.18  0.07  0.06  0.04  0.03 -0.01  0.06  0.12  0.08  0.12 0.72 0.278 2.5

                        MR1  MR2  MR3  MR4  MR5  MR7  MR6  MR9  MR8 MR10 MR12 MR11 MR13
SS loadings           11.73 8.81 3.15 0.97 0.67 0.46 0.41 0.31 0.28 0.25 0.24 0.24 0.17
Proportion Var         0.29 0.22 0.08 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00
Cumulative Var         0.29 0.51 0.59 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.68 0.69 0.69
Proportion Explained   0.42 0.32 0.11 0.04 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Proportion  0.42 0.74 0.86 0.89 0.92 0.93 0.95 0.96 0.97 0.98 0.99 0.99 1.00

Mean item complexity =  2.4
Test of the hypothesis that 13 factors are sufficient.

The degrees of freedom for the null model are  780  and the objective function was  34.22 with Chi Square of  13170.85
The degrees of freedom for the model are 338  and the objective function was  0.95 

The root mean square of the residuals (RMSR) is  0.01 
The df corrected root mean square of the residuals is  0.02 

The harmonic number of observations is  400 with the empirical chi square  79.31  with prob <  1 
The total number of observations was  400  with MLE Chi Square =  358.44  with prob <  0.21 

Tucker Lewis Index of factoring reliability =  0.996
RMSEA index =  0.018  and the 90 % confidence intervals are  NA 0.023
BIC =  -1666.68
Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                                MR1  MR2  MR3  MR4  MR5  MR7  MR6  MR9  MR8  MR10  MR12  MR11  MR13
Correlation of scores with factors             1.00 0.99 0.94 0.89 0.85 0.77 0.81 0.72 0.73  0.69  0.65  0.68  0.61
Multiple R square of scores with factors       1.00 0.97 0.89 0.80 0.73 0.59 0.65 0.52 0.53  0.48  0.42  0.46  0.37
Minimum correlation of possible factor scores  0.99 0.95 0.78 0.59 0.45 0.18 0.31 0.05 0.06 -0.04 -0.16 -0.09 -0.25
Factor Analysis using method =  minres
Call: fa(r = d3_all, nfactors = 13, rotate = "none", fm = "minres", 
    cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
                 MR1   MR2   MR3   MR4   MR5   MR7   MR6   MR9   MR8  MR10  MR12  MR11  MR13   h2    u2 com
happy           0.63  0.62 -0.13 -0.05 -0.20  0.01 -0.24 -0.07 -0.07  0.00  0.02 -0.01  0.00 0.91 0.088 2.6
depressed       0.50  0.60 -0.24  0.16 -0.10  0.00  0.12  0.08 -0.01  0.05 -0.04  0.05 -0.01 0.74 0.262 2.7
fear            0.80  0.27 -0.02 -0.19 -0.04 -0.03  0.03  0.00  0.02  0.04  0.03  0.06  0.02 0.76 0.236 1.4
angry           0.62  0.53 -0.14 -0.12 -0.09 -0.01  0.06 -0.02  0.03  0.02  0.01 -0.07  0.06 0.72 0.275 2.3
calm            0.66  0.47  0.05 -0.14 -0.05 -0.07 -0.06  0.04  0.04  0.12 -0.05  0.12  0.04 0.72 0.282 2.2
sounds          0.18  0.24  0.61  0.00 -0.22  0.25 -0.04  0.07 -0.06 -0.08 -0.17 -0.04  0.03 0.63 0.371 2.6
seeing          0.35  0.15  0.54  0.00 -0.10  0.18 -0.05  0.07  0.01  0.02 -0.05 -0.01  0.02 0.49 0.514 2.4
temperature     0.38  0.15  0.59  0.00 -0.19  0.13  0.14 -0.08 -0.01 -0.14  0.09  0.07  0.03 0.63 0.375 2.7
odors           0.54  0.12  0.41  0.03 -0.07  0.29  0.04 -0.05  0.06 -0.01  0.12  0.04 -0.04 0.59 0.410 2.9
depth           0.23  0.30  0.50  0.08 -0.08  0.04  0.21  0.00 -0.01 -0.06 -0.08  0.10 -0.10 0.48 0.521 3.0
computations   -0.75  0.31  0.15  0.17 -0.03 -0.22  0.03 -0.01  0.02 -0.21  0.04  0.08  0.02 0.82 0.176 2.0
thoughts        0.58  0.57  0.10 -0.14  0.12 -0.04 -0.13  0.10  0.12 -0.03 -0.10 -0.03  0.00 0.76 0.239 2.6
reasoning      -0.02  0.61  0.35  0.17  0.08 -0.20 -0.07  0.11  0.05  0.02 -0.05  0.01 -0.06 0.59 0.405 2.3
remembering    -0.10  0.47  0.49  0.10 -0.07 -0.12  0.11 -0.04 -0.05  0.06 -0.02 -0.20  0.06 0.57 0.435 2.9
beliefs         0.25  0.59 -0.13  0.19  0.19  0.08  0.10 -0.25 -0.10 -0.07 -0.08  0.01  0.00 0.59 0.408 2.7
hungry          0.99 -0.15  0.01  0.03  0.00 -0.01  0.00  0.00  0.00  0.00  0.00  0.00  0.00 1.00 0.005 1.0
tired           0.85  0.17 -0.04 -0.12 -0.03  0.01  0.09  0.00 -0.06  0.04  0.08  0.16  0.03 0.81 0.187 1.3
pain            0.89  0.01  0.00 -0.15 -0.02  0.10  0.05  0.04  0.00  0.01  0.03  0.00 -0.04 0.83 0.167 1.1
nauseated       0.70  0.38 -0.12 -0.08 -0.12 -0.04  0.24  0.18 -0.06  0.01 -0.01  0.10 -0.05 0.78 0.222 2.2
safe            0.72  0.29  0.04 -0.13  0.01  0.03 -0.05  0.07  0.02  0.08  0.04  0.00  0.07 0.65 0.351 1.5
love            0.56  0.61 -0.14  0.07 -0.01 -0.03  0.08  0.15 -0.21 -0.03  0.02 -0.15 -0.07 0.81 0.190 2.7
recognizing    -0.19  0.57  0.45  0.18 -0.11 -0.06 -0.08  0.14  0.09  0.09 -0.02  0.00  0.01 0.65 0.352 2.9
communicating  -0.05  0.39  0.47  0.19 -0.03 -0.08  0.00 -0.02 -0.01  0.15  0.11 -0.04  0.04 0.45 0.548 2.8
guilt           0.36  0.64 -0.24  0.22  0.01  0.10  0.05  0.01  0.06 -0.04 -0.03 -0.04  0.01 0.66 0.335 2.3
disrespected    0.37  0.63 -0.23  0.21  0.04  0.14  0.18 -0.06  0.03  0.05 -0.13 -0.06  0.09 0.72 0.277 2.8
free_will       0.73  0.35  0.05 -0.23  0.25  0.05 -0.08  0.05 -0.18 -0.14 -0.02  0.02  0.07 0.84 0.159 2.3
choices         0.31  0.41  0.46 -0.05  0.13 -0.12 -0.05 -0.06 -0.01  0.06  0.02  0.04  0.09 0.53 0.468 3.4
self_restraint  0.29  0.58  0.02  0.03  0.14 -0.11  0.06 -0.07 -0.15  0.15 -0.11  0.05  0.02 0.53 0.474 2.2
intentions      0.57  0.37  0.22 -0.20  0.22 -0.10  0.08 -0.05  0.07  0.00  0.04 -0.13  0.03 0.64 0.357 3.2
goal            0.20  0.41  0.43  0.02 -0.04 -0.09  0.13 -0.13 -0.11  0.09  0.12 -0.11  0.05 0.48 0.517 3.7
conscious       0.73  0.36  0.05 -0.19  0.15  0.03 -0.02  0.01  0.09 -0.02 -0.05  0.01 -0.16 0.76 0.239 1.9
self_aware      0.54  0.48  0.06 -0.15  0.18  0.04 -0.06  0.04  0.15  0.02 -0.06  0.09  0.10 0.64 0.362 2.8
desires         0.75  0.39  0.02 -0.28  0.06 -0.01  0.15 -0.10  0.08 -0.04  0.06 -0.06 -0.05 0.84 0.156 2.1
embarrassed     0.35  0.60 -0.28  0.25  0.03  0.10  0.00  0.07  0.21 -0.15  0.06 -0.04  0.08 0.73 0.274 3.3
emo_recog       0.24  0.66  0.07  0.21  0.26  0.09 -0.13  0.02 -0.01  0.09  0.16 -0.01 -0.15 0.69 0.310 2.4
joy             0.62  0.61 -0.15 -0.02 -0.20 -0.07 -0.02 -0.06  0.06 -0.07 -0.03  0.02 -0.08 0.84 0.162 2.5
morality        0.05  0.58  0.14  0.35  0.26  0.04 -0.06 -0.19 -0.07  0.05 -0.08  0.15 -0.11 0.63 0.366 3.0
personality     0.33  0.67  0.09  0.05  0.10  0.01 -0.07  0.11  0.07  0.01  0.12 -0.05 -0.01 0.61 0.386 1.8
pleasure        0.77  0.40 -0.07 -0.12 -0.08 -0.06  0.09 -0.11  0.05  0.05 -0.06 -0.02  0.02 0.81 0.194 1.8
pride           0.40  0.65 -0.24  0.18  0.07  0.06  0.04  0.03 -0.01  0.06  0.12  0.08  0.12 0.72 0.278 2.5

                        MR1  MR2  MR3  MR4  MR5  MR7  MR6  MR9  MR8 MR10 MR12 MR11 MR13
SS loadings           11.73 8.81 3.15 0.97 0.67 0.46 0.41 0.31 0.28 0.25 0.24 0.24 0.17
Proportion Var         0.29 0.22 0.08 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00
Cumulative Var         0.29 0.51 0.59 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.68 0.69 0.69
Proportion Explained   0.42 0.32 0.11 0.04 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Proportion  0.42 0.74 0.86 0.89 0.92 0.93 0.95 0.96 0.97 0.98 0.99 0.99 1.00

Mean item complexity =  2.4
Test of the hypothesis that 13 factors are sufficient.

The degrees of freedom for the null model are  780  and the objective function was  34.22 with Chi Square of  13170.85
The degrees of freedom for the model are 338  and the objective function was  0.95 

The root mean square of the residuals (RMSR) is  0.01 
The df corrected root mean square of the residuals is  0.02 

The harmonic number of observations is  400 with the empirical chi square  79.31  with prob <  1 
The total number of observations was  400  with MLE Chi Square =  358.44  with prob <  0.21 

Tucker Lewis Index of factoring reliability =  0.996
RMSEA index =  0.018  and the 90 % confidence intervals are  NA 0.023
BIC =  -1666.68
Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                                MR1  MR2  MR3  MR4  MR5  MR7  MR6  MR9  MR8  MR10  MR12  MR11  MR13
Correlation of scores with factors             1.00 0.99 0.94 0.89 0.85 0.77 0.81 0.72 0.73  0.69  0.65  0.68  0.61
Multiple R square of scores with factors       1.00 0.97 0.89 0.80 0.73 0.59 0.65 0.52 0.53  0.48  0.42  0.46  0.37
Minimum correlation of possible factor scores  0.99 0.95 0.78 0.59 0.45 0.18 0.31 0.05 0.06 -0.04 -0.16 -0.09 -0.25
[1] 3
[1] 3

Step 2: Run EFA with varimax rotation

Factor Analysis using method =  minres
Call: fa(r = d3_all, nfactors = nfactors_d3_all, rotate = chosenRotType, 
    fm = "minres", cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
                 MR1   MR2   MR3   h2   u2 com
happy           0.52  0.68  0.17 0.77 0.23 2.0
depressed       0.35  0.75  0.06 0.69 0.31 1.4
fear            0.78  0.37  0.10 0.75 0.25 1.5
angry           0.54  0.62  0.11 0.69 0.31 2.0
calm            0.59  0.49  0.25 0.66 0.34 2.3
sounds          0.20 -0.03  0.64 0.45 0.55 1.2
seeing          0.37 -0.04  0.55 0.43 0.57 1.8
temperature     0.40 -0.06  0.58 0.50 0.50 1.8
odors           0.54  0.04  0.41 0.47 0.53 1.9
depth           0.20  0.11  0.58 0.38 0.62 1.3
computations   -0.80  0.08  0.27 0.72 0.28 1.3
thoughts        0.50  0.55  0.33 0.67 0.33 2.6
reasoning      -0.13  0.41  0.59 0.54 0.46 1.9
remembering    -0.15  0.19  0.65 0.49 0.51 1.3
beliefs         0.10  0.64  0.15 0.44 0.56 1.2
hungry          0.93  0.10 -0.05 0.87 0.13 1.0
tired           0.82  0.31  0.04 0.78 0.22 1.3
pain            0.90  0.16  0.01 0.84 0.16 1.1
nauseated       0.64  0.50  0.06 0.66 0.34 1.9
safe            0.69  0.35  0.16 0.63 0.37 1.6
love            0.42  0.70  0.15 0.70 0.30 1.8
recognizing    -0.27  0.29  0.66 0.59 0.41 1.7
communicating  -0.11  0.16  0.60 0.40 0.60 1.2
guilt           0.19  0.76  0.07 0.63 0.37 1.1
disrespected    0.21  0.75  0.07 0.61 0.39 1.2
free_will       0.69  0.40  0.18 0.66 0.34 1.8
choices         0.27  0.21  0.59 0.47 0.53 1.7
self_restraint  0.19  0.56  0.27 0.43 0.57 1.7
intentions      0.54  0.33  0.34 0.52 0.48 2.4
goal            0.16  0.21  0.56 0.39 0.61 1.5
conscious       0.69  0.41  0.20 0.68 0.32 1.8
self_aware      0.49  0.48  0.26 0.54 0.46 2.5
desires         0.73  0.44  0.18 0.75 0.25 1.8
embarrassed     0.18  0.75  0.02 0.60 0.40 1.1
emo_recog       0.09  0.62  0.35 0.51 0.49 1.6
joy             0.50  0.70  0.14 0.76 0.24 1.9
morality       -0.10  0.51  0.37 0.41 0.59 1.9
personality     0.21  0.62  0.37 0.57 0.43 1.9
pleasure        0.70  0.51  0.11 0.76 0.24 1.9
pride           0.24  0.77  0.08 0.66 0.34 1.2

                       MR1  MR2  MR3
SS loadings           9.87 9.20 4.98
Proportion Var        0.25 0.23 0.12
Cumulative Var        0.25 0.48 0.60
Proportion Explained  0.41 0.38 0.21
Cumulative Proportion 0.41 0.79 1.00

Mean item complexity =  1.7
Test of the hypothesis that 3 factors are sufficient.

The degrees of freedom for the null model are  780  and the objective function was  34.22 with Chi Square of  13170.85
The degrees of freedom for the model are 663  and the objective function was  3.58 

The root mean square of the residuals (RMSR) is  0.03 
The df corrected root mean square of the residuals is  0.03 

The harmonic number of observations is  400 with the empirical chi square  526.02  with prob <  1 
The total number of observations was  400  with MLE Chi Square =  1371.31  with prob <  1.4e-51 

Tucker Lewis Index of factoring reliability =  0.932
RMSEA index =  0.054  and the 90 % confidence intervals are  0.048 0.056
BIC =  -2601.03
Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                                MR1  MR2  MR3
Correlation of scores with factors             0.98 0.96 0.94
Multiple R square of scores with factors       0.96 0.93 0.88
Minimum correlation of possible factor scores  0.92 0.86 0.77

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Factor loadings table

Study 4

Design: 21 conditions, between-subjects Date conducted: 2016-01-14

Demographics

Joining, by = c("condition", "min_age", "max_age", "median_age", "mean_age", "sd_age")
joining character vector and factor, coercing into character vector

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Exploratory factor analysis

Step 1: Run maximal EFA (without and with rotation)

Parallel analysis suggests that the number of factors =  4  and the number of components =  3 

Factor Analysis using method =  minres
Call: fa(r = d4_all, nfactors = 13, rotate = "none", fm = "minres", 
    cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
                MR1   MR2   MR3   MR5   MR4   MR6   MR7  MR10   MR8   MR9  MR11  MR13  MR12   h2    u2 com
happy          0.90 -0.04 -0.19 -0.06 -0.21 -0.05  0.01 -0.01 -0.01  0.10  0.00  0.04 -0.07 0.91 0.085 1.3
depressed      0.81  0.20 -0.08 -0.06 -0.07  0.23 -0.15  0.03  0.00 -0.12 -0.03  0.05  0.14 0.83 0.167 1.6
fear           0.86 -0.34 -0.11  0.05  0.12  0.00 -0.04  0.01 -0.06  0.03 -0.01  0.00  0.05 0.88 0.118 1.4
angry          0.87 -0.04 -0.09 -0.06 -0.09  0.09 -0.05  0.10  0.08 -0.05 -0.02 -0.11 -0.06 0.82 0.178 1.2
calm           0.87 -0.18 -0.10  0.06 -0.02 -0.06 -0.04 -0.01 -0.08  0.08  0.05  0.09  0.02 0.82 0.178 1.2
sounds         0.69 -0.33  0.40 -0.22  0.12 -0.06  0.10  0.00  0.03  0.06  0.06  0.01  0.09 0.84 0.159 2.6
seeing         0.76 -0.33  0.28 -0.13  0.11  0.01  0.02  0.04  0.08  0.04  0.06 -0.08 -0.05 0.82 0.178 1.9
temperature    0.63 -0.32  0.39 -0.14  0.16  0.00  0.03 -0.01  0.00  0.04  0.08  0.05  0.06 0.71 0.290 2.6
odors          0.79 -0.25  0.19 -0.07  0.14  0.11  0.05 -0.04  0.12 -0.09  0.06 -0.06  0.06 0.79 0.209 1.6
depth          0.67 -0.05  0.43  0.09  0.10  0.00 -0.05  0.11  0.12  0.03  0.08  0.17 -0.02 0.72 0.277 2.2
computations   0.18  0.53  0.51 -0.13 -0.03  0.02  0.07  0.04  0.03  0.12  0.06  0.13 -0.04 0.64 0.363 2.7
thoughts       0.85  0.01 -0.02  0.11 -0.06 -0.09  0.15 -0.12  0.04 -0.21 -0.08  0.04  0.06 0.84 0.158 1.4
reasoning      0.70  0.32  0.33  0.18 -0.05  0.01  0.09 -0.09  0.06 -0.05 -0.01 -0.02 -0.11 0.76 0.235 2.3
remembering    0.72  0.02  0.47 -0.06 -0.10  0.06 -0.05  0.02  0.09 -0.03 -0.09  0.06  0.03 0.78 0.219 1.9
beliefs        0.66  0.55 -0.01  0.16  0.05 -0.05  0.18 -0.02 -0.03 -0.02  0.11  0.06  0.00 0.81 0.186 2.4
hungry         0.80 -0.41 -0.15  0.06  0.21  0.00  0.04  0.01 -0.01  0.03 -0.03 -0.02 -0.02 0.88 0.120 1.8
tired          0.87 -0.34 -0.13 -0.04  0.06  0.05 -0.01  0.00 -0.04 -0.01 -0.01  0.01 -0.10 0.92 0.084 1.4
pain           0.83 -0.39 -0.17 -0.03  0.19  0.07  0.08 -0.01 -0.04 -0.03 -0.04  0.01  0.00 0.93 0.067 1.7
nauseated      0.83 -0.09 -0.14 -0.04 -0.04  0.11 -0.10 -0.02  0.09 -0.01  0.06  0.07 -0.01 0.76 0.239 1.2
safe           0.86 -0.19 -0.07  0.07  0.00 -0.16 -0.10 -0.02 -0.09  0.11  0.00  0.04  0.13 0.86 0.139 1.3
love           0.86  0.08 -0.18 -0.04 -0.20 -0.01  0.09  0.07 -0.03  0.00  0.02 -0.01 -0.02 0.83 0.172 1.3
recognizing    0.78 -0.05  0.29 -0.28 -0.18  0.03  0.06 -0.01  0.01  0.02 -0.02  0.02 -0.03 0.81 0.190 1.7
communicating  0.72 -0.11  0.47 -0.13 -0.12 -0.11 -0.06  0.00 -0.23 -0.10 -0.03 -0.06 -0.04 0.87 0.125 2.3
guilt          0.74  0.51 -0.08 -0.05  0.05  0.08  0.01 -0.04  0.02  0.01 -0.01 -0.01  0.03 0.83 0.167 1.9
disrespected   0.73  0.45 -0.02  0.02  0.09  0.12 -0.01  0.06 -0.13  0.03  0.08 -0.11  0.02 0.80 0.205 2.0
free_will      0.81 -0.08  0.04  0.31  0.03 -0.06  0.02  0.14  0.06  0.01 -0.05 -0.04  0.07 0.81 0.195 1.4
choices        0.76 -0.06  0.39  0.15  0.02 -0.03 -0.08 -0.01  0.09  0.11 -0.15 -0.10  0.02 0.82 0.181 1.9
self_restraint 0.73  0.35  0.20  0.17  0.01  0.26 -0.08 -0.13 -0.11  0.09 -0.04  0.06 -0.04 0.84 0.156 2.3
intentions     0.80  0.04  0.13  0.22  0.09 -0.13 -0.13  0.03  0.09  0.00  0.03 -0.01 -0.01 0.76 0.242 1.4
goal           0.64  0.07  0.38  0.20  0.06 -0.10 -0.13 -0.16 -0.02 -0.11 -0.04 -0.01 -0.08 0.68 0.320 2.4
conscious      0.82 -0.14  0.00  0.13  0.02 -0.08  0.07 -0.09 -0.04 -0.04  0.05  0.01  0.00 0.73 0.270 1.2
self_aware     0.75  0.19  0.11  0.26  0.02 -0.01  0.15  0.12  0.03  0.07 -0.13  0.04 -0.08 0.75 0.248 1.7
desires        0.88 -0.08 -0.05  0.13 -0.05 -0.05 -0.13 -0.03  0.03 -0.10  0.19 -0.03 -0.03 0.87 0.132 1.3
embarrassed    0.72  0.59 -0.14 -0.15  0.20 -0.09 -0.04 -0.01  0.02 -0.01 -0.03  0.00 -0.01 0.96 0.043 2.4
emo_recog      0.76  0.35  0.08 -0.01 -0.06  0.09  0.04  0.12 -0.07  0.00 -0.01 -0.03  0.02 0.74 0.262 1.6
joy            0.90 -0.01 -0.16 -0.04 -0.25 -0.02  0.02 -0.10  0.07  0.07 -0.01 -0.06  0.05 0.93 0.069 1.3
morality       0.68  0.50  0.11  0.11  0.01  0.10  0.16 -0.04 -0.07  0.07  0.10 -0.09  0.04 0.80 0.199 2.3
personality    0.89  0.02 -0.04 -0.04 -0.20 -0.02  0.04  0.11 -0.02 -0.12 -0.02  0.06  0.03 0.87 0.126 1.2
pleasure       0.91 -0.19 -0.10  0.00 -0.05  0.01 -0.04  0.00  0.09  0.00  0.01  0.03 -0.05 0.89 0.113 1.2
pride          0.81  0.33 -0.06  0.11 -0.06 -0.01  0.00  0.17 -0.05  0.00  0.04 -0.03  0.00 0.81 0.192 1.5

                        MR1  MR2  MR3  MR5  MR4  MR6  MR7 MR10  MR8  MR9 MR11 MR13 MR12
SS loadings           24.34 3.28 2.17 0.72 0.52 0.32 0.28 0.22 0.22 0.22 0.17 0.15 0.13
Proportion Var         0.61 0.08 0.05 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00
Cumulative Var         0.61 0.69 0.74 0.76 0.78 0.78 0.79 0.80 0.80 0.81 0.81 0.82 0.82
Proportion Explained   0.74 0.10 0.07 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.00
Cumulative Proportion  0.74 0.84 0.91 0.93 0.95 0.96 0.97 0.97 0.98 0.99 0.99 1.00 1.00

Mean item complexity =  1.7
Test of the hypothesis that 13 factors are sufficient.

The degrees of freedom for the null model are  780  and the objective function was  52.07 with Chi Square of  21651.8
The degrees of freedom for the model are 338  and the objective function was  1.2 

The root mean square of the residuals (RMSR) is  0.01 
The df corrected root mean square of the residuals is  0.01 

The harmonic number of observations is  431 with the empirical chi square  39.58  with prob <  1 
The total number of observations was  431  with MLE Chi Square =  486.84  with prob <  1.9e-07 

Tucker Lewis Index of factoring reliability =  0.983
RMSEA index =  0.035  and the 90 % confidence intervals are  0.025 0.038
BIC =  -1563.5
Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                                MR1  MR2  MR3  MR5  MR4  MR6  MR7 MR10  MR8  MR9  MR11  MR13  MR12
Correlation of scores with factors             1.00 0.98 0.96 0.90 0.91 0.82 0.79 0.74 0.76 0.76  0.70  0.66  0.68
Multiple R square of scores with factors       0.99 0.96 0.92 0.80 0.83 0.67 0.62 0.54 0.58 0.58  0.49  0.44  0.46
Minimum correlation of possible factor scores  0.99 0.93 0.84 0.61 0.66 0.35 0.23 0.09 0.16 0.15 -0.01 -0.12 -0.08
Factor Analysis using method =  minres
Call: fa(r = d4_all, nfactors = 13, rotate = "none", fm = "minres", 
    cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
                MR1   MR2   MR3   MR5   MR4   MR6   MR7  MR10   MR8   MR9  MR11  MR13  MR12   h2    u2 com
happy          0.90 -0.04 -0.19 -0.06 -0.21 -0.05  0.01 -0.01 -0.01  0.10  0.00  0.04 -0.07 0.91 0.085 1.3
depressed      0.81  0.20 -0.08 -0.06 -0.07  0.23 -0.15  0.03  0.00 -0.12 -0.03  0.05  0.14 0.83 0.167 1.6
fear           0.86 -0.34 -0.11  0.05  0.12  0.00 -0.04  0.01 -0.06  0.03 -0.01  0.00  0.05 0.88 0.118 1.4
angry          0.87 -0.04 -0.09 -0.06 -0.09  0.09 -0.05  0.10  0.08 -0.05 -0.02 -0.11 -0.06 0.82 0.178 1.2
calm           0.87 -0.18 -0.10  0.06 -0.02 -0.06 -0.04 -0.01 -0.08  0.08  0.05  0.09  0.02 0.82 0.178 1.2
sounds         0.69 -0.33  0.40 -0.22  0.12 -0.06  0.10  0.00  0.03  0.06  0.06  0.01  0.09 0.84 0.159 2.6
seeing         0.76 -0.33  0.28 -0.13  0.11  0.01  0.02  0.04  0.08  0.04  0.06 -0.08 -0.05 0.82 0.178 1.9
temperature    0.63 -0.32  0.39 -0.14  0.16  0.00  0.03 -0.01  0.00  0.04  0.08  0.05  0.06 0.71 0.290 2.6
odors          0.79 -0.25  0.19 -0.07  0.14  0.11  0.05 -0.04  0.12 -0.09  0.06 -0.06  0.06 0.79 0.209 1.6
depth          0.67 -0.05  0.43  0.09  0.10  0.00 -0.05  0.11  0.12  0.03  0.08  0.17 -0.02 0.72 0.277 2.2
computations   0.18  0.53  0.51 -0.13 -0.03  0.02  0.07  0.04  0.03  0.12  0.06  0.13 -0.04 0.64 0.363 2.7
thoughts       0.85  0.01 -0.02  0.11 -0.06 -0.09  0.15 -0.12  0.04 -0.21 -0.08  0.04  0.06 0.84 0.158 1.4
reasoning      0.70  0.32  0.33  0.18 -0.05  0.01  0.09 -0.09  0.06 -0.05 -0.01 -0.02 -0.11 0.76 0.235 2.3
remembering    0.72  0.02  0.47 -0.06 -0.10  0.06 -0.05  0.02  0.09 -0.03 -0.09  0.06  0.03 0.78 0.219 1.9
beliefs        0.66  0.55 -0.01  0.16  0.05 -0.05  0.18 -0.02 -0.03 -0.02  0.11  0.06  0.00 0.81 0.186 2.4
hungry         0.80 -0.41 -0.15  0.06  0.21  0.00  0.04  0.01 -0.01  0.03 -0.03 -0.02 -0.02 0.88 0.120 1.8
tired          0.87 -0.34 -0.13 -0.04  0.06  0.05 -0.01  0.00 -0.04 -0.01 -0.01  0.01 -0.10 0.92 0.084 1.4
pain           0.83 -0.39 -0.17 -0.03  0.19  0.07  0.08 -0.01 -0.04 -0.03 -0.04  0.01  0.00 0.93 0.067 1.7
nauseated      0.83 -0.09 -0.14 -0.04 -0.04  0.11 -0.10 -0.02  0.09 -0.01  0.06  0.07 -0.01 0.76 0.239 1.2
safe           0.86 -0.19 -0.07  0.07  0.00 -0.16 -0.10 -0.02 -0.09  0.11  0.00  0.04  0.13 0.86 0.139 1.3
love           0.86  0.08 -0.18 -0.04 -0.20 -0.01  0.09  0.07 -0.03  0.00  0.02 -0.01 -0.02 0.83 0.172 1.3
recognizing    0.78 -0.05  0.29 -0.28 -0.18  0.03  0.06 -0.01  0.01  0.02 -0.02  0.02 -0.03 0.81 0.190 1.7
communicating  0.72 -0.11  0.47 -0.13 -0.12 -0.11 -0.06  0.00 -0.23 -0.10 -0.03 -0.06 -0.04 0.87 0.125 2.3
guilt          0.74  0.51 -0.08 -0.05  0.05  0.08  0.01 -0.04  0.02  0.01 -0.01 -0.01  0.03 0.83 0.167 1.9
disrespected   0.73  0.45 -0.02  0.02  0.09  0.12 -0.01  0.06 -0.13  0.03  0.08 -0.11  0.02 0.80 0.205 2.0
free_will      0.81 -0.08  0.04  0.31  0.03 -0.06  0.02  0.14  0.06  0.01 -0.05 -0.04  0.07 0.81 0.195 1.4
choices        0.76 -0.06  0.39  0.15  0.02 -0.03 -0.08 -0.01  0.09  0.11 -0.15 -0.10  0.02 0.82 0.181 1.9
self_restraint 0.73  0.35  0.20  0.17  0.01  0.26 -0.08 -0.13 -0.11  0.09 -0.04  0.06 -0.04 0.84 0.156 2.3
intentions     0.80  0.04  0.13  0.22  0.09 -0.13 -0.13  0.03  0.09  0.00  0.03 -0.01 -0.01 0.76 0.242 1.4
goal           0.64  0.07  0.38  0.20  0.06 -0.10 -0.13 -0.16 -0.02 -0.11 -0.04 -0.01 -0.08 0.68 0.320 2.4
conscious      0.82 -0.14  0.00  0.13  0.02 -0.08  0.07 -0.09 -0.04 -0.04  0.05  0.01  0.00 0.73 0.270 1.2
self_aware     0.75  0.19  0.11  0.26  0.02 -0.01  0.15  0.12  0.03  0.07 -0.13  0.04 -0.08 0.75 0.248 1.7
desires        0.88 -0.08 -0.05  0.13 -0.05 -0.05 -0.13 -0.03  0.03 -0.10  0.19 -0.03 -0.03 0.87 0.132 1.3
embarrassed    0.72  0.59 -0.14 -0.15  0.20 -0.09 -0.04 -0.01  0.02 -0.01 -0.03  0.00 -0.01 0.96 0.043 2.4
emo_recog      0.76  0.35  0.08 -0.01 -0.06  0.09  0.04  0.12 -0.07  0.00 -0.01 -0.03  0.02 0.74 0.262 1.6
joy            0.90 -0.01 -0.16 -0.04 -0.25 -0.02  0.02 -0.10  0.07  0.07 -0.01 -0.06  0.05 0.93 0.069 1.3
morality       0.68  0.50  0.11  0.11  0.01  0.10  0.16 -0.04 -0.07  0.07  0.10 -0.09  0.04 0.80 0.199 2.3
personality    0.89  0.02 -0.04 -0.04 -0.20 -0.02  0.04  0.11 -0.02 -0.12 -0.02  0.06  0.03 0.87 0.126 1.2
pleasure       0.91 -0.19 -0.10  0.00 -0.05  0.01 -0.04  0.00  0.09  0.00  0.01  0.03 -0.05 0.89 0.113 1.2
pride          0.81  0.33 -0.06  0.11 -0.06 -0.01  0.00  0.17 -0.05  0.00  0.04 -0.03  0.00 0.81 0.192 1.5

                        MR1  MR2  MR3  MR5  MR4  MR6  MR7 MR10  MR8  MR9 MR11 MR13 MR12
SS loadings           24.34 3.28 2.17 0.72 0.52 0.32 0.28 0.22 0.22 0.22 0.17 0.15 0.13
Proportion Var         0.61 0.08 0.05 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00
Cumulative Var         0.61 0.69 0.74 0.76 0.78 0.78 0.79 0.80 0.80 0.81 0.81 0.82 0.82
Proportion Explained   0.74 0.10 0.07 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.00
Cumulative Proportion  0.74 0.84 0.91 0.93 0.95 0.96 0.97 0.97 0.98 0.99 0.99 1.00 1.00

Mean item complexity =  1.7
Test of the hypothesis that 13 factors are sufficient.

The degrees of freedom for the null model are  780  and the objective function was  52.07 with Chi Square of  21651.8
The degrees of freedom for the model are 338  and the objective function was  1.2 

The root mean square of the residuals (RMSR) is  0.01 
The df corrected root mean square of the residuals is  0.01 

The harmonic number of observations is  431 with the empirical chi square  39.58  with prob <  1 
The total number of observations was  431  with MLE Chi Square =  486.84  with prob <  1.9e-07 

Tucker Lewis Index of factoring reliability =  0.983
RMSEA index =  0.035  and the 90 % confidence intervals are  0.025 0.038
BIC =  -1563.5
Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                                MR1  MR2  MR3  MR5  MR4  MR6  MR7 MR10  MR8  MR9  MR11  MR13  MR12
Correlation of scores with factors             1.00 0.98 0.96 0.90 0.91 0.82 0.79 0.74 0.76 0.76  0.70  0.66  0.68
Multiple R square of scores with factors       0.99 0.96 0.92 0.80 0.83 0.67 0.62 0.54 0.58 0.58  0.49  0.44  0.46
Minimum correlation of possible factor scores  0.99 0.93 0.84 0.61 0.66 0.35 0.23 0.09 0.16 0.15 -0.01 -0.12 -0.08
[1] 3
[1] 3

Step 2: Run EFA with varimax rotation

Factor Analysis using method =  minres
Call: fa(r = d4_all, nfactors = 4, rotate = chosenRotType, fm = "minres", 
    cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
                 MR1  MR2  MR3   MR4   h2   u2 com
happy           0.73 0.47 0.22  0.28 0.89 0.11 2.3
depressed       0.50 0.61 0.23  0.17 0.71 0.29 2.4
fear            0.83 0.22 0.36 -0.07 0.88 0.12 1.5
angry           0.67 0.45 0.31  0.19 0.78 0.22 2.4
calm            0.75 0.36 0.31  0.04 0.79 0.21 1.8
sounds          0.45 0.08 0.75  0.08 0.77 0.23 1.7
seeing          0.56 0.14 0.67  0.04 0.79 0.21 2.0
temperature     0.41 0.07 0.71 -0.01 0.68 0.32 1.6
odors           0.58 0.23 0.57  0.00 0.73 0.27 2.3
depth           0.29 0.34 0.67 -0.07 0.66 0.34 1.9
computations   -0.41 0.49 0.40  0.15 0.59 0.41 3.1
thoughts        0.60 0.50 0.33  0.06 0.72 0.28 2.6
reasoning       0.17 0.68 0.47  0.00 0.71 0.29 1.9
remembering     0.25 0.40 0.71  0.17 0.76 0.24 2.0
beliefs         0.19 0.84 0.12 -0.05 0.76 0.24 1.1
hungry          0.85 0.13 0.32 -0.14 0.87 0.13 1.4
tired           0.85 0.21 0.35  0.05 0.90 0.10 1.5
pain            0.87 0.16 0.32 -0.06 0.89 0.11 1.3
nauseated       0.69 0.39 0.26  0.14 0.72 0.28 2.0
safe            0.74 0.34 0.35  0.01 0.78 0.22 1.9
love            0.64 0.55 0.17  0.27 0.82 0.18 2.5
recognizing     0.41 0.35 0.60  0.38 0.81 0.19 3.3
communicating   0.34 0.29 0.71  0.19 0.74 0.26 2.0
guilt           0.30 0.82 0.13  0.10 0.79 0.21 1.4
disrespected    0.29 0.78 0.18  0.02 0.73 0.27 1.4
free_will       0.60 0.45 0.37 -0.15 0.72 0.28 2.8
choices         0.38 0.39 0.66 -0.06 0.74 0.26 2.3
self_restraint  0.24 0.72 0.37 -0.02 0.71 0.29 1.8
intentions      0.49 0.51 0.43 -0.14 0.70 0.30 3.1
goal            0.23 0.44 0.57 -0.13 0.58 0.42 2.4
conscious       0.65 0.38 0.37 -0.04 0.70 0.30 2.3
self_aware      0.39 0.62 0.34 -0.10 0.66 0.34 2.4
desires         0.68 0.45 0.33  0.03 0.78 0.22 2.3
embarrassed     0.28 0.82 0.09  0.07 0.76 0.24 1.3
emo_recog       0.31 0.71 0.29  0.14 0.71 0.29 1.8
joy             0.70 0.50 0.24  0.27 0.88 0.12 2.5
morality        0.17 0.81 0.24  0.01 0.75 0.25 1.3
personality     0.63 0.52 0.33  0.25 0.83 0.17 2.9
pleasure        0.78 0.36 0.35  0.12 0.87 0.13 1.9
pride           0.43 0.74 0.19  0.07 0.78 0.22 1.8

                        MR1   MR2  MR3  MR4
SS loadings           12.14 10.24 7.24 0.79
Proportion Var         0.30  0.26 0.18 0.02
Cumulative Var         0.30  0.56 0.74 0.76
Proportion Explained   0.40  0.34 0.24 0.03
Cumulative Proportion  0.40  0.74 0.97 1.00

Mean item complexity =  2
Test of the hypothesis that 4 factors are sufficient.

The degrees of freedom for the null model are  780  and the objective function was  52.07 with Chi Square of  21651.8
The degrees of freedom for the model are 626  and the objective function was  4.01 

The root mean square of the residuals (RMSR) is  0.02 
The df corrected root mean square of the residuals is  0.02 

The harmonic number of observations is  431 with the empirical chi square  237.73  with prob <  1 
The total number of observations was  431  with MLE Chi Square =  1656.76  with prob <  4.1e-94 

Tucker Lewis Index of factoring reliability =  0.938
RMSEA index =  0.064  and the 90 % confidence intervals are  0.058 0.065
BIC =  -2140.62
Fit based upon off diagonal values = 1
Measures of factor score adequacy             
                                                MR1  MR2  MR3  MR4
Correlation of scores with factors             0.98 0.97 0.96 0.88
Multiple R square of scores with factors       0.96 0.95 0.91 0.77
Minimum correlation of possible factor scores  0.92 0.90 0.83 0.54

back to TOC

Factor loadings table

Big factor loadings table for all studies (Studies 1-4)

Joining, by = "mc"
Joining, by = "mc"
Joining, by = "mc"

Figures

Joining, by = "condition"
joining character vector and factor, coercing into character vectorJoining, by = "condition"
joining character vector and factor, coercing into character vectorJoining, by = "condition"
joining character vector and factor, coercing into character vectorJoining, by = "condition"
joining character vector and factor, coercing into character vector
Joining, by = c("condition", "mc", "ci_lower", "mean", "ci_upper", "n", "study")
Joining, by = c("condition", "mc", "ci_lower", "mean", "ci_upper", "n", "study")
Joining, by = c("condition", "mc", "ci_lower", "mean", "ci_upper", "n", "study")
Joining, by = "mc"
Joining, by = "mc"
Joining, by = "mc"
Joining, by = "mc"
Joining, by = "mc"

Figure 1

Mean ratings of 40 mental capacities for a subset of the 21 entities included in Study 4. (See Fig. S5 for mean ratings for the full set of entities.) Participants responded on a scale from 0 (Not at all capable) to 6 (Highly capable). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 1. Doing computations was the only item to load negatively on its dominant factor in any study (and did so in Studies 1-3); in Study 4, it loaded positively on its dominant factor (Factor 2, “heart”).

Figure S1 (supporting materials)

Factor loadings for the 40 mental capacities on the three rotated factors in Study 1. Items are colored by their dominant factor loading: Items that loaded most strongly on the body factor (physiological states and will) are in red; items that loaded most strongly on the heart factor (social-emotional experiences and morality) are in blue; and items that loaded most strongly on the mind factor (perceptual-cognitive abilities and goal pursuit) are in green.

Joining, by = "item"

Figure S2 (supporting materials)

Mean ratings of 40 mental capacities for the 2 entities included in Studies 1-3. Participants responded on a scale from 0 (Not at all capable) to 6 (Highly capable). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 1.

Figure S3 (supporting materials)

Mean ratings of 40 mental capacities for all 21 entities included in Study 4. Participants responded on a scale from 0 (Not at all capable) to 6 (Highly capable). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 4.

Figure S7 (supporting materials)

Mean ratings of 40 mental capacities for all of the entities included in Studies 1-4. Participants responded on a scale from 0 (Not at all capable) to 6 (Highly capable). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 1.

---
title: 'Body, Heart, & Mind (Weisman, Dweck, & Markman, submitted)'
output:
  html_notebook:
    theme: flatly
    toc: yes
  pdf_document:
    toc: yes
---

```{r global_options, include = FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
```

# Setup

```{r workspace setup}
# load libraries
library(tidyverse)
library(psych)
library(langcog) # source: https://github.com/langcog/langcog
library(scatterplot3d)
library(lattice)
library(directlabels)
library(plotly)
library(RColorBrewer)

# clear workspace
rm(list = ls(all = T))
graphics.off()
```

```{r functions}
# make na.rm = T versions of summary functions
mean_na <- function(x) {mean(x, na.rm = T)}
ci_lower_na <- function(x) {quantile(x, 0.025, na.rm = T)}
ci_upper_na <- function(x) {quantile(x, 0.975, na.rm = T)}

# make rounding function
round2 <- function(x) {format(round(x, 2), nsmall = 2)}

# make cleanup function
cleanup <- function(datasource) {
  if(datasource %in% c("study 1", "study 2")) {
    
    # set target dataset
    if(datasource == "study 1"){d <- d_raw_study1}
    if(datasource == "study 2"){d <- d_raw_study2}
    
    # enact exclusionary criteria
    d_clean_1 <- d %>%
      mutate(finished_mod = ifelse(is.na(CATCH), 0,
                                   ifelse(finished == 1, 1,
                                          0.5))) %>%
      filter(CATCH == 1, # exclude Ps who fail catch trials 
             finished_mod != 0) %>% # exclude Ps who did not complete task
      mutate(yob_correct = as.numeric(
        ifelse(as.numeric(as.character(yob)) > 1900 & 
                 as.numeric(as.character(yob)) < 2000, 
               as.numeric(as.character(yob)), NA)), # correct formatting in yob
        age_approx = 2016 - yob_correct) %>% # calculate approximate age
      mutate(gender = factor(gender, levels = c(1, 2, 0), 
                             labels = c("m", "f", "other"))) %>%
      filter(age_approx >= 18) # exclude Ps who are younger than 18 years
    
    # recode background and demographic variables
    d_clean <- d_clean_1 %>%
      mutate( # deal with study number
        study = factor(study)) %>%
      mutate( # deal with study duration
        duration = as.numeric(difftime(strptime(end_time, "%I:%M:%S"),
                                       strptime(start_time, "%I:%M:%S"),
                                       units = "mins"))) %>%
      mutate( # deal with race
        race_asian_east = 
          factor(ifelse(is.na(race_asian_east), "", "asian_east ")),
        race_asian_south = 
          factor(ifelse(is.na(race_asian_south), "", "asian_south ")),
        race_asian_other = 
          factor(ifelse(is.na(race_asian_other), "", "asian_other ")),
        race_black = 
          factor(ifelse(is.na(race_black), "", "black ")),
        race_hispanic = 
          factor(ifelse(is.na(race_hispanic), "", "hispanic ")),
        race_middle_eastern = 
          factor(ifelse(is.na(race_middle_eastern), "", "middle_eastern ")),
        race_native_american = 
          factor(ifelse(is.na(race_native_american), "", "native_american ")),
        race_pac_islander = 
          factor(ifelse(is.na(race_pac_islander), "", "pac_islander ")),
        race_white = 
          factor(ifelse(is.na(race_white), "", "white ")),
        race_other_prefno = 
          factor(ifelse(is.na(race_other_prefno), "", "other_prefno ")),
        race_cat = paste0(race_asian_east, race_asian_south, race_asian_other,
                          race_black, race_hispanic, race_middle_eastern,
                          race_native_american, race_pac_islander, race_white,
                          race_other_prefno),
        race_cat2 = factor(sub(" +$", "", race_cat)),
        race_cat3 = factor(ifelse(grepl(" ", race_cat2) == T, "multiracial",
                                  as.character(race_cat2)))) %>%
      dplyr::select(study, subid:end_time, duration, finished:gender, 
             religion_buddhism:age_approx, race_cat3) %>%
      rename(race_cat = race_cat3) %>%
      mutate( # deal with religion
        religion_buddhism = 
          factor(ifelse(is.na(religion_buddhism), "", "buddhism ")),
        religion_christianity = 
          factor(ifelse(is.na(religion_christianity), "", "christianity ")),
        religion_hinduism = 
          factor(ifelse(is.na(religion_hinduism), "", "hinduism ")),
        religion_islam = 
          factor(ifelse(is.na(religion_islam), "", "islam ")),
        religion_jainism = 
          factor(ifelse(is.na(religion_jainism), "", "jainism ")),
        religion_judaism = 
          factor(ifelse(is.na(religion_judaism), "", "judaism ")),
        religion_sikhism = 
          factor(ifelse(is.na(religion_sikhism), "", "sikhism ")),
        religion_other = 
          factor(ifelse(is.na(religion_other), "", "other ")),
        religion_none = 
          factor(ifelse(is.na(religion_none), "", "none ")),
        religion_prefno = 
          factor(ifelse(is.na(religion_prefno), "", "other_prefno ")),
        religion_cat = paste0(religion_buddhism, religion_christianity, 
                              religion_hinduism, religion_islam, 
                              religion_jainism, religion_judaism, 
                              religion_sikhism, religion_other, 
                              religion_none, religion_prefno),
        religion_cat2 = factor(sub(" +$", "", religion_cat)),
        religion_cat3 = factor(ifelse(grepl(" ", religion_cat2) == T, 
                                      "multireligious",
                                      as.character(religion_cat2)))) %>%
      dplyr::select(study:gender, feedback:race_cat, religion_cat3) %>%
      rename(religion_cat = religion_cat3)
    
    # remove extraneous dfs and variables
    rm(d, d_clean_1)
  }
  
  if(datasource == "study 3") {
    
    # set target dataset
    d <- d_raw_study3
    
    # enact exclusionary criteria
    d_clean_1 <- d %>%
      mutate(finished_mod = ifelse(is.na(CATCH..characterL) | 
                                     is.na(CATCH..characterR), 0,
                                   ifelse(finished == 1, 1,
                                          0.5))) %>%
      filter(CATCH..characterL == 5, # exclude Ps who fail catch trials 
             CATCH..characterR == 5,
             finished_mod != 0) %>% # exclude Ps who did not complete task
      mutate(yob_correct = as.numeric(
        ifelse(as.numeric(as.character(yob)) > 1900 & 
                 as.numeric(as.character(yob)) < 2000, 
               as.numeric(as.character(yob)), NA)), # correct formatting in yob
        age_approx = 2016 - yob_correct) %>% # calculate approximate age
      mutate(gender = factor(gender, levels = c(1, 2, 0), 
                             labels = c("m", "f", "other"))) %>%
      filter(age_approx >= 18) # exclude Ps who are younger than 18 years
    
    # recode background and demographic variables
    d_clean_2 <- d_clean_1 %>%
      mutate( # deal with study number
        study = factor(study)) %>%
      mutate( # deal with study duration
        duration = as.numeric(difftime(strptime(end_time, "%I:%M:%S"),
                                       strptime(start_time, "%I:%M:%S"),
                                       units = "mins"))) %>%
      mutate( # deal with race
        race_asian_east = 
          factor(ifelse(is.na(race_asian_east), "", "asian_east ")),
        race_asian_south = 
          factor(ifelse(is.na(race_asian_south), "", "asian_south ")),
        race_asian_other = 
          factor(ifelse(is.na(race_asian_other), "", "asian_other ")),
        race_black = 
          factor(ifelse(is.na(race_black), "", "black ")),
        race_hispanic = 
          factor(ifelse(is.na(race_hispanic), "", "hispanic ")),
        race_middle_eastern = 
          factor(ifelse(is.na(race_middle_eastern), "", "middle_eastern ")),
        race_native_american = 
          factor(ifelse(is.na(race_native_american), "", "native_american ")),
        race_pac_islander = 
          factor(ifelse(is.na(race_pac_islander), "", "pac_islander ")),
        race_white = 
          factor(ifelse(is.na(race_white), "", "white ")),
        race_other_prefno = 
          factor(ifelse(is.na(race_other_prefno), "", "other_prefno ")),
        race_cat = paste0(race_asian_east, race_asian_south, race_asian_other,
                          race_black, race_hispanic, race_middle_eastern,
                          race_native_american, race_pac_islander, race_white,
                          race_other_prefno),
        race_cat2 = factor(sub(" +$", "", race_cat)),
        race_cat3 = factor(ifelse(grepl(" ", race_cat2) == T, "multiracial",
                                  as.character(race_cat2)))) %>%
      dplyr::select(study, subid:end_time, duration, finished:gender, 
             religion_buddhism:age_approx, race_cat3) %>%
      rename(race_cat = race_cat3) %>%
      mutate( # deal with religion
        religion_buddhism = 
          factor(ifelse(is.na(religion_buddhism), "", "buddhism ")),
        religion_christianity = 
          factor(ifelse(is.na(religion_christianity), "", "christianity ")),
        religion_hinduism = 
          factor(ifelse(is.na(religion_hinduism), "", "hinduism ")),
        religion_islam = 
          factor(ifelse(is.na(religion_islam), "", "islam ")),
        religion_jainism = 
          factor(ifelse(is.na(religion_jainism), "", "jainism ")),
        religion_judaism = 
          factor(ifelse(is.na(religion_judaism), "", "judaism ")),
        religion_sikhism = 
          factor(ifelse(is.na(religion_sikhism), "", "sikhism ")),
        religion_other = 
          factor(ifelse(is.na(religion_other), "", "other ")),
        religion_none = 
          factor(ifelse(is.na(religion_none), "", "none ")),
        religion_prefno = 
          factor(ifelse(is.na(religion_prefno), "", "other_prefno ")),
        religion_cat = paste0(religion_buddhism, religion_christianity, 
                              religion_hinduism, religion_islam, 
                              religion_jainism, religion_judaism, 
                              religion_sikhism, religion_other, 
                              religion_none, religion_prefno),
        religion_cat2 = factor(sub(" +$", "", religion_cat)),
        religion_cat3 = factor(ifelse(grepl(" ", religion_cat2) == T, 
                                      "multireligious",
                                      as.character(religion_cat2)))) %>%
      dplyr::select(study:gender, feedback:race_cat, religion_cat3) %>%
      rename(religion_cat = religion_cat3)
    
    # rename response variables
    d_clean_3 <- d_clean_2
    names(d_clean_3) <- gsub("get", "", names(d_clean_3))
    names(d_clean_3) <- gsub("\\.", "", names(d_clean_3))
    names(d_clean_3) <- gsub("char", "_char", names(d_clean_3))
    names(d_clean_3)[names(d_clean_3) %in% c("_characterL", "_characterR")] <- 
      c("characterL", "characterR")
    
    # recode response variables (center)
    d_clean_4 <- d_clean_3
    for(i in 11:92) {
      d_clean_4[,i] <- d_clean_4[,i] - 4 # transform from 1 to 7 --> -3 to 3
    }
    
    # recode characterL vs. characterR as beetle vs. robot
    d_clean_5_demo <- d_clean_4 %>%
      dplyr::select(study:condition, yob:religion_cat)
    
    d_clean_5_characterL <- d_clean_4 %>%
      mutate(target = characterL) %>%
      dplyr::select(study, subid, target, happy_characterL:CATCH_characterL)
    names(d_clean_5_characterL) <- gsub("_characterL", "", 
                                        names(d_clean_5_characterL))
    
    d_clean_5_characterR <- d_clean_4 %>%
      mutate(target = characterR) %>%
      dplyr::select(study, subid, target, happy_characterR:CATCH_characterR)
    names(d_clean_5_characterR) <- gsub("_characterR", "", 
                                        names(d_clean_5_characterR))
    
    d_clean <- d_clean_5_characterL %>%
      full_join(d_clean_5_characterR) %>%
      full_join(d_clean_5_demo) %>%
      dplyr::select(study, subid, date:religion_cat, target:CATCH)
    
    # remove extraneous dfs and variables
    rm(d, d_clean_1, d_clean_2, d_clean_3, d_clean_4, d_clean_5_characterL, 
       d_clean_5_characterR, d_clean_5_demo, i)
  }
  
  if(datasource == "study 4") {
    
    # set target dataset
    d <- d_raw_study4

        # enact exclusionary criteria
    d_clean_1 <- d %>%
      mutate(finished_mod = ifelse(is.na(CATCH), 0,
                                   ifelse(finished == 1, 1,
                                          0.5))) %>%
      filter(CATCH == 1, # exclude Ps who fail catch trials 
             finished_mod != 0) %>% # exclude Ps who did not complete task
      mutate(yob_correct = as.numeric(
        ifelse(as.numeric(as.character(yob)) > 1900 & 
                 as.numeric(as.character(yob)) < 2000, 
               as.numeric(as.character(yob)), NA)), # correct formatting in yob
        age_approx = 2016 - yob_correct) %>% # calculate approximate age
      mutate(gender = factor(gender, levels = c(1, 2, 0), 
                             labels = c("m", "f", "other"))) %>%
      filter(age_approx >= 18) # exclude Ps who are younger than 18 years
    
    # recode one character
    d_clean_2 <- d_clean_1 %>%
      mutate(condition = factor(ifelse(
        grepl("vegetative", as.character(condition)), "pvs",
        ifelse(grepl("blue", as.character(condition)), "bluejay",
               ifelse(grepl("chimp", as.character(condition)), "chimp",
                      as.character(condition))))))

    # recode background and demographic variables
    d_clean <- d_clean_2 %>%
      mutate( # deal with study number
        study = factor(study)) %>%
      mutate( # deal with study duration
        duration = as.numeric(difftime(strptime(end_time, "%I:%M:%S"),
                                       strptime(start_time, "%I:%M:%S"),
                                       units = "mins"))) %>%
      mutate( # deal with race
        race_asian_east = 
          factor(ifelse(is.na(race_asian_east), "", "asian_east ")),
        race_asian_south = 
          factor(ifelse(is.na(race_asian_south), "", "asian_south ")),
        race_asian_other = 
          factor(ifelse(is.na(race_asian_other), "", "asian_other ")),
        race_black = 
          factor(ifelse(is.na(race_black), "", "black ")),
        race_hispanic = 
          factor(ifelse(is.na(race_hispanic), "", "hispanic ")),
        race_middle_eastern = 
          factor(ifelse(is.na(race_middle_eastern), "", "middle_eastern ")),
        race_native_american = 
          factor(ifelse(is.na(race_native_american), "", "native_american ")),
        race_pac_islander = 
          factor(ifelse(is.na(race_pac_islander), "", "pac_islander ")),
        race_white = 
          factor(ifelse(is.na(race_white), "", "white ")),
        race_other_prefno = 
          factor(ifelse(is.na(race_other_prefno), "", "other_prefno ")),
        race_cat = paste0(race_asian_east, race_asian_south, race_asian_other,
                          race_black, race_hispanic, race_middle_eastern,
                          race_native_american, race_pac_islander, race_white,
                          race_other_prefno),
        race_cat2 = factor(sub(" +$", "", race_cat)),
        race_cat3 = factor(ifelse(grepl(" ", race_cat2) == T, "multiracial",
                                  as.character(race_cat2)))) %>%
      dplyr::select(study, subid:end_time, duration, finished:gender, 
             education:age_approx, race_cat3) %>%
      rename(race_cat = race_cat3)
    
    # filter conditions if desired
    if(is.element("none", chosenExclude)) {} else {
      d_clean <- d_clean %>%
        filter(!condition %in% chosenExclude)
    }
    
    # remove extraneous dfs and variables
    rm(d, d_clean_1, d_clean_2)
  }
  
#   # transform to 0 to 6 scale
#   d_clean <- d_clean %>%
#     gather(mc, score, happy:pride) %>%
#     mutate(score = score + 3) %>% # transform from -3 to 3 --> 0 to 6
#     spread(mc, score)
  
  # remove outliers
  if(chosenOutlierHandling == "remove") {
    
    if(datasource %in% c("study 1", "study 2", "study 4")) {
        d_clean <- d_clean %>%
        gather(mc, score, happy:pride) %>%
        group_by(condition, mc) %>%
        filter(!score %in% boxplot.stats(score, 2.5)$out) %>%
        spread(mc, score) %>%
        arrange(condition, subid)
    }
    
    if(datasource == "study 3") {
      d_clean <- d_clean %>%
        gather(mc, score, happy:pride) %>%
        group_by(target, mc) %>%
        filter(!score %in% boxplot.stats(score, 2.5)$out) %>%
        spread(mc, score) %>%
        arrange(target, subid)
    }
    
  }
  
  # filter items if desired
  if(is.element("none", chosenExcludeItem)) {} else {
    d_clean <- d_clean %>%
      dplyr::select(-contains(chosenExcludeItem))
  }

  # return cleaned dataset
  return(d_clean)
}

# make function for examining exclusion of participants
excludedCounts <- function(datasource) {
  
  # set datasource
  if(datasource == "study 1"){
    d <- d1
    d_raw <- d_raw_study1
  }
  if(datasource == "study 2"){
    d <- d2
    d_raw <- d_raw_study2
  }
  if(datasource == "study 3"){
    d <- d3
    d_raw <- d_raw_study3
  }
  if(datasource == "study 4"){
    d <- d4
    d_raw <- d_raw_study4
  }
  
  # get subids of successful participants
  d_subids <- levels(factor(as.character(d$subid)))
  
  # get subids of excluded participants
  d_excluded <- d_raw %>%
    filter(is.element(subid, d_subids) == FALSE) %>%
    dplyr::select(condition, subid, finished, starts_with("CATCH"), yob)

  # count excluded participants
  d_excluded_n <- length(d_excluded$subid)
  
  if(datasource %in% c("study 1", "study 2", "study 4")) {
    # count participants who did not finish
    d_excluded_unfinished <- d_excluded %>%
      filter(is.na(CATCH) == T,
             finished != 1) %>%
      dplyr::select(subid) %>%
      c()
    
    # count participants who finished, but failed catch trial
    d_excluded_CATCH <- d_excluded %>%
      filter(is.element(subid, d_excluded_unfinished$subid) == FALSE) %>%
      filter(CATCH != 1) %>%
      dplyr::select(subid) %>%
      c()
  }
  
  if(datasource == "study 3") {
    # count participants who did not finish
    d_excluded_unfinished <- d_excluded %>%
      filter(is.na(CATCH..characterL) == T,
             is.na(CATCH..characterR) == T,
             finished != 1) %>%
      dplyr::select(subid) %>%
      c()
    
    # count participants who finished, but failed catch trial
    d_excluded_CATCH <- d_excluded %>%
      filter(is.element(subid, d_excluded_unfinished$subid) == FALSE) %>%
      filter(CATCH..characterL != 5 | CATCH..characterR != 5) %>%
      dplyr::select(subid) %>%
      c()
  }
  
  # count participants who finished and passed catch trial, 
  # but did not provide year of birth
  d_excluded_no_yob <- d_excluded %>%
    filter(is.element(subid, d_excluded_unfinished$subid) == FALSE,
           is.element(subid, d_excluded_CATCH$subid) == FALSE) %>%
    mutate(yob = as.numeric(as.character(yob))) %>%
    filter(is.na(yob) | yob < 1899 | nchar(as.character(yob)) != 4) %>%
    dplyr::select(subid) %>%
    c()
  
  # count participants who finished and passed catch trial, 
  # but did not provide year of birth
  d_excluded_young <- d_excluded %>%
    filter(is.element(subid, d_excluded_unfinished$subid) == FALSE,
           is.element(subid, d_excluded_CATCH$subid) == FALSE,
           is.element(subid, d_excluded_no_yob$subid) == FALSE) %>%
    mutate(yob = as.numeric(as.character(yob))) %>%
    filter(is.na(yob) | 2016 - yob < 18) %>%
    dplyr::select(subid) %>%
    c()
  
  # sum up excluded participants by category
  total <- sum(length(d_excluded_unfinished$subid),
               length(d_excluded_CATCH$subid),
               length(d_excluded_no_yob$subid),
               length(d_excluded_young$subid))
  
  # calculate counts
  excluded_counts <- 
    data.frame("did_not_finish" = length(d_excluded_unfinished$subid),
               "failed_catch_trial" = length(d_excluded_CATCH$subid),
               "did_not_provide_yob" = length(d_excluded_no_yob$subid),
               "too_young" = length(d_excluded_young$subid),
               "TOTAL_excluded" = total,
               "TOTAL_participated" = length(d$subid),
               "OVERALL_TOTAL" = sum(total, length(d$subid)))
  
  if(total != d_excluded_n) {
    stop("Error: 4 sources of exclusion do not add up to total.")
    } else {
      return(excluded_counts)
    }
}

# make function for stripping dataframes for dimension reducation
makeDRDF <- function(datasource, chosenCondition) {
  
  # set target dataset
  if(datasource == "study 1"){d <- d1}
  if(datasource == "study 2"){d <- d2}
  if(datasource == "study 3"){
    # rename variables for ease of function applpication
    d <- d3 %>%
      rename(order = condition,
             condition = target)
    
    # rename subids by condition if collapses across conditions
    d <- d %>%
      mutate(subid = paste(condition, subid, sep = "_"))
  }
  if(datasource == "study 4"){d <- d4}
  
  # filter by condition if specified
  if(chosenCondition %in% c("beetle", "robot")) {
    d <- d %>% filter(condition == chosenCondition)
  }
  
  # make stripped dataframe for dimension reducation analyses
  d_strip <- d %>%
    dplyr::select(subid, happy:pride)
  d_strip <- data.frame(d_strip[,-1], row.names = d_strip$subid)
  
  # return stripped dataframe
  return(d_strip)
}

# make demographics functions
demoSampleSize <- function(datasource) {

  # set target dataset
  if(datasource == "study 1"){d <- d1}
  if(datasource == "study 2"){d <- d2}
  if(datasource == "study 3"){
    # rename variables for ease of function applpication
    d <- d3 %>%
      rename(order = condition,
             condition = target)
  }
  if(datasource == "study 4"){d <- d4}

  # get sample size per condition
  sample_size <- vector()
  for(i in levels(d$condition)) {
    sample_size[as.character(i)] <- 
      as.numeric(d %>% filter(condition == i) %>% dplyr::select(subid) %>% 
                   unique() %>% count())
  }

  # add total sample size  
  sample_size["all"] <- as.numeric(d %>% dplyr::select(subid) %>% 
                                     unique() %>% count())
  
  # make into dataframe for using kable
  sample_size <- data.frame(sample_size) %>%
    rownames_to_column() %>%
    rename(condition = rowname,
           n = sample_size)
  
  # return dataframe for using kable
  return(sample_size)
}
demoDuration <- function(datasource) {

  # set target dataset
  if(datasource == "study 1"){d <- d1}
  if(datasource == "study 2"){d <- d2}
  if(datasource == "study 3"){
    # recode variables for ease of function applpication
    d <- d3 %>%
      mutate(condition = "within-subjects")
  }
  if(datasource == "study 4"){d <- d4}

  # get sample size per condition
  duration <- d %>%
    group_by(condition) %>%
    summarise(min_duration = min(duration),
              max_duration = max(duration),
              median_duration = median(duration),
              mean_duration = mean(duration),
              sd_duration = sd(duration))

  # add total duration
  if(datasource %in% c("study 1", "study 2", "study 4")) {
    all <- d %>%
      summarise(min_duration = min(duration),
                max_duration = max(duration),
                median_duration = median(duration),
                mean_duration = mean(duration),
                sd_duration = sd(duration)) %>%
      mutate(condition = "all")
    duration <- rbind(duration, all) # not sure why full_join doesn't work    
  }

  # return dataframe for using kable
  return(duration)
}
demoAge <- function(datasource) {

  # set target dataset
  if(datasource == "study 1"){d <- d1}
  if(datasource == "study 2"){d <- d2}
  if(datasource == "study 3"){
    # recode variables for ease of function applpication
    d <- d3 %>%
      mutate(condition = "within-subjects")
  }
  if(datasource == "study 4"){d <- d4}

  # get sample size per condition
  age <- d %>%
    group_by(condition) %>%
    summarise(min_age = min(age_approx),
              max_age = max(age_approx),
              median_age = median(age_approx),
              mean_age = mean(age_approx),
              sd_age = sd(age_approx))

  # add total age
  if(datasource %in% c("study 1", "study 2", "study 4")) {
    all <- d %>%
      summarise(min_age = min(age_approx),
                max_age = max(age_approx),
                median_age = median(age_approx),
                mean_age = mean(age_approx),
                sd_age = sd(age_approx)) %>%
      mutate(condition = "all")
    age <- full_join(age, all)
  }

  # return dataframe for using kable
  return(age)
}
demoGender <- function(datasource) {

  # set target dataset
  if(datasource == "study 1"){d <- d1}
  if(datasource == "study 2"){d <- d2}
  if(datasource == "study 3"){d <- d3}
  if(datasource == "study 4"){d <- d4}

  # get gender per condition and overall
  if(datasource %in% c("study 1", "study 2", "study 4")) {
    gender <- data.frame(addmargins(with(d, table(condition, gender)))) %>%
      filter(gender != "Sum") %>%
      rename(n = Freq)
  }
  
  if(datasource == "study 3") {
    gender <- data.frame(with(d, table(gender))) %>%
      rename(n = Freq) %>%
      mutate(condition = "Sum") %>%
      dplyr::select(condition, gender, n)
  }
  
  if(datasource %in% c("study 1", "study 2", "study 3")) {
    gender <- gender %>%
    mutate(condition = factor(ifelse(condition == "Sum", 
                                     "all", as.character(condition)),
                              levels = c("beetle", "robot", "all"))) %>%
    arrange(condition, gender) %>%
    spread(gender, n)
  }
  
  if(datasource == "study 4") {
    gender <- gender %>%
    mutate(condition = factor(ifelse(condition == "Sum", 
                                     "all", as.character(condition)),
                              levels = c(levels(d$condition), "all"))) %>%
    arrange(condition, gender) %>%
    spread(gender, n)
  }
  
  # return dataframe for using kable
  return(gender)
}
demoRace <- function(datasource) {

  # set target dataset
  if(datasource == "study 1"){d <- d1}
  if(datasource == "study 2"){d <- d2}
  if(datasource == "study 3"){d <- d3}
  if(datasource == "study 4"){d <- d4}

  # get race per condition and overall
  if(datasource %in% c("study 1", "study 2", "study 4")) {
    race <- data.frame(addmargins(with(d, table(condition, race_cat)))) %>%
      filter(race_cat != "Sum") %>%
      rename(n = Freq)
  }
  
  if(datasource == "study 3") {
    race <- data.frame(with(d, table(race_cat))) %>%
      rename(n = Freq) %>%
      mutate(condition = "Sum") %>%
      dplyr::select(condition, race_cat, n)
  }
  
  if(datasource %in% c("study 1", "study 2", "study 3")) {
    race <- race %>%
    mutate(condition = factor(ifelse(condition == "Sum", 
                                     "all", as.character(condition)),
                              levels = c("beetle", "robot", "all"))) %>%
    arrange(condition, race_cat) %>%
    spread(race_cat, n)
  }
  
  if(datasource == "study 4") {
    race <- race %>%
    mutate(condition = factor(ifelse(condition == "Sum", 
                                     "all", as.character(condition)),
                              levels = c(levels(d$condition), "all"))) %>%
    arrange(condition, race_cat) %>%
    spread(race_cat, n)
  }
  
  # return dataframe for using kable
  return(race)
}
demoReligion <- function(datasource) {

  # set target dataset
  if(datasource == "study 1"){d <- d1}
  if(datasource == "study 2"){d <- d2}
  if(datasource == "study 3"){d <- d3}

  # get religion per condition and overall
  if(datasource %in% c("study 1", "study 2")) {
    religion <- data.frame(addmargins(with(d, table(condition, religion_cat)))) %>%
      filter(religion_cat != "Sum") %>%
      rename(n = Freq)
  }
  
  if(datasource == "study 3") {
    religion <- data.frame(with(d, table(religion_cat))) %>%
      rename(n = Freq) %>%
      mutate(condition = "Sum") %>%
      dplyr::select(condition, religion_cat, n)
  }
  
  if(datasource %in% c("study 1", "study 2", "study 3")) {
    religion <- religion %>%
    mutate(condition = factor(ifelse(condition == "Sum", 
                                     "all", as.character(condition)),
                              levels = c("beetle", "robot", "all"))) %>%
    arrange(condition, religion_cat) %>%
    spread(religion_cat, n)
  }
  
  # return dataframe for using kable
  if(datasource == "study 4"){
    stop("Religion information not available for Study 4")
  } else {return(religion)}
}
demoEducation <- function(datasource) {

  # set target dataset
  if(datasource == "study 4"){d <- d4}
  
  # get education per condition and overall
  if(datasource == "study 4") {
    education <- 
      data.frame(addmargins(with(d, table(condition, education)))) %>%
      filter(education != "Sum") %>%
      rename(n = Freq) %>%
      mutate(condition = factor(ifelse(condition == "Sum",
                                       "all", as.character(condition)),
                                levels = c(levels(d$condition), "all")),
             education = factor(education,
                                levels = c(1:7, 0),
                                labels = c("some_HS",
                                           "HS_diploma",
                                           "some_college",
                                           "associates",
                                           "bachelors",
                                           "some_grad",
                                           "grad",
                                           "pref_no"))) %>%
      arrange(condition, education) %>%
      spread(education, n)
  }
  
  # return dataframe for using kable
  if(datasource %in% c("study 1", "study 2", "study 3")){
    stop("Education information not available for Studies 1-3")
  } else {return(education)}
}

# plotting functions
makeFacetLabs <- function(df_plotting) {
  facet_labels <- array()
  df_plotting <- df_plotting %>% mutate(condition = factor(condition))
  for(i in 1:length(levels(df_plotting$condition))) {
    df <- df_plotting %>% filter(condition == levels(df_plotting$condition)[i]) %>%
      select(condition, n) %>% unique()
    facet_labels[i] <- paste0(df$condition, " (n = ", df$n, ")")
  }
  names(facet_labels) <- levels(df_plotting$condition)
  return(facet_labels)
}
```

```{r modeling decisions}
# remove outliers?
chosenOutlierHandling <- "keep"
# chosenOutlierHandling <- "remove"

# exclude any conditions in study 4?
chosenExclude <- "none"
# chosenExclude <- c("stapler", "car", "computer")

# exclude any items?
chosenExcludeItem <- "none"
# chosenExcludeItem <- "computations"

# NOTE: always choose minimal residual (fm = "minres") instead of ML because of non-normality

# for EFAs, what kind of correlation?
chosenCorType <- "cor" # pearson correlation
# chosenCorType <- "poly" # polychoric correlation

# for EFAs, what kind of rotation?
chosenRotType <- "varimax" # varimax rotation
# chosenRotType <- "oblimin" # oblimin rotation
# chosenRotType <- "none" # no rotation

data.frame("conditionsExcluded" = chosenExclude,
           "outlierHandling" = chosenOutlierHandling,
           "EFA_correlation" = chosenCorType,
           "EFA_rotation" = chosenRotType)
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

# Data preparation

```{r data upload}
# study 1 (2015-12-15, 2 conditions, between-subjects)
d_raw_study1 <- read.csv("http://stanford.edu/~kweisman/experiments/bodyheartmind/bodyheartmind_study1.csv") %>%
  mutate(study = "study 1")

# study 2 (2016-01-12, 2 conditions, between-subjects - REPLICATION)
d_raw_study2 <- read.csv("http://stanford.edu/~kweisman/experiments/bodyheartmind/bodyheartmind_study2.csv") %>%
  mutate(study = "study 2")

# study 3 (2016-01-10, 2 conditions, within-subjects)
d_raw_study3 <- read.csv("http://stanford.edu/~kweisman/experiments/bodyheartmind/bodyheartmind_study3.csv") %>%
  mutate(study = "study 3")

# study 4 (2016-01-14, 21 conditions, between-subjects)
d_raw_study4 <- read.csv("http://stanford.edu/~kweisman/experiments/bodyheartmind/bodyheartmind_study4.csv") %>%
  mutate(study = "study 4")
```

```{r data cleanup}
# clean up datasets
d1 <- cleanup("study 1")
d2 <- cleanup("study 2")
d3 <- cleanup("study 3")
d4 <- cleanup("study 4")
```

```{r dataframes for dimension reducation}
# make dataframes for s1
# d1_beetle <- makeDRDF("study 1", "beetle")
# d1_robot <- makeDRDF("study 1", "robot")
d1_all <- makeDRDF("study 1", "all")

# make dataframes for study 2
# d2_beetle <- makeDRDF("study 2", "beetle")
# d2_robot <- makeDRDF("study 2", "robot")
d2_all <- makeDRDF("study 2", "all")

# make dataframes for study 3
# d3_beetle <- makeDRDF("study 3", "beetle")
# d3_robot <- makeDRDF("study 3", "robot")
d3_all <- makeDRDF("study 3", "all")

# make dataframes for study 4
d4_all <- makeDRDF("study 4", "all")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

# Analysis plan

For all studies we conduct exploratory factor analyses using Pearson correlations to find minimum residual solutions. 

For each study, we first examine maximal unrotated and rotated solutions. To determine the maximum number of factors to extract, we use the following rule of thumb: With $p$ observations per participant, we can extract a maximum of $k$ factors, where $(p-k)*2 > p+k$, i.e., $k < p/3$. Thus, with 40 mental capacity items, we can extract a maximum of 13 factors.

To determine how many factors to retain, we use the following preset retention criteria, considering the unrotated maximal solution:

  - Each factor must have an eigenvalue >1.0.
  - Each factor must individually account for >5% of the total variance in the maximal model.
  - Each factor must be the dominant factor (i.e., the factor with the highest factor loading) for ≥1 mental capacity item.

We then examine and interpret varimax-rotated solutions, extracting only the number of factors that meet these criteria.

*Note*: For Studies 1-2, we initially planned to conduct dimension reduction analyses for each condition (beetle vs. robot) separately. However, we now consider this analysis plan to have been fundamentally flawed: Each of these separate analyses is only capable of surfacing factors that highlight substantial disagreement among participants within that condition thus failing to capture key differences in attributions of mental capacities to beetles vs. robots, with no formal means of synthesizing results across conditions. Nonetheless, the results of these analyses are generally consistent with the findings reported here: The most prominent and reliable finding within each condition is that participants distinguish between emotional and perceptual varieties of experience. See <a href="https://osf.io/zd3mu", target="blank">https://osf.io/zd3mu</a> for the preregistered analyses, included analysis scripts.

# Study 1

Design: 2 conditions (beetle, robot), between-subjects
Date conducted: 2015-12-15

## Demographics

```{r s1 demographics}
# examine exclusion
excludedCounts("study 1")

# make demographics tables
demoSampleSize("study 1")
demoDuration("study 1")
demoAge("study 1")
demoGender("study 1")
demoRace("study 1")
demoReligion("study 1")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

## Exploratory factor analysis

### Step 1: Run maximal EFA (without and with rotation)

```{r s1 all no rotation}
# examine scree plot
fa.parallel(d1_all)

# run EFA without rotation with N factors
efa_d1_all_unrotated <- fa(d1_all, 13, rotate = "none",
                           cor = chosenCorType, fm = "minres")
print(efa_d1_all_unrotated)

# examine eigenvalues and variance explained
efa_d1_all_unrotated_eigenvalues <- print(efa_d1_all_unrotated)$Vaccounted %>%
  t() %>%
  data.frame()

# count factors with eigenvalues > 1 and variance explained > 5%
efa_d1_all_unrotated_nfactors <- efa_d1_all_unrotated_eigenvalues %>%
  filter(SS.loadings > 1, Proportion.Explained > 0.05) %>%
  count() %>%
  as.numeric()
efa_d1_all_unrotated_nfactors
```

```{r s1 all rotation}
efa_d1_all_rotated <- fa(d1_all, efa_d1_all_unrotated_nfactors, rotate = chosenRotType,
                           cor = chosenCorType, fm = "minres")

# check that each of these factors is the dominant factor for at least one mental capacity item
efa_d1_all_rotated_loadings <- fa.sort(loadings(efa_d1_all_rotated)[]) %>%
  data.frame() %>%
  rownames_to_column("capacity") %>%
  gather(factor, loading, -capacity) %>%
  mutate(loading_abs = abs(loading)) %>%
  group_by(capacity) %>%
  top_n(1, loading_abs) %>%
  ungroup()
efa_d1_all_rotated_loadings

# drop any factors where n < 1
efa_d1_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0)

# set number of factors to extract
nfactors_d1_all <- efa_d1_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0) %>%
  nrow()
nfactors_d1_all
```

### Step 2: Run EFA with varimax rotation

```{r s1 all varimax rotation}
# run EFA with rotation with N factors
efa_d1_all_rotatedN <- fa(d1_all, nfactors_d1_all, 
                          rotate = chosenRotType, cor = chosenCorType, fm = "minres")
print(efa_d1_all_rotatedN)

# get loadings for each factor
efa_d1_all_rotatedN_loadings <- loadings(efa_d1_all_rotatedN)[] %>%
  data.frame() %>% 
  rownames_to_column(var = "mc")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

#### Factor loadings table

```{r s1 loadings table}
data.frame(loadings(fa.sort(efa_d1_all_rotatedN))[]) %>%
  rownames_to_column("capacity") %>%
  mutate_at(vars(starts_with("M")), funs(round2))
```

# Study 2

Design: 2 conditions (beetle, robot), between-subjects (replication of Study 1)
Date conducted: 2016-01-12

## Demographics

```{r s2 demographics}
# examine exclusion
excludedCounts("study 2")

# make demographics tables
demoSampleSize("study 2")
demoDuration("study 2")
demoAge("study 2")
demoGender("study 2")
demoRace("study 2")
demoReligion("study 2")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

## Exploratory factor analysis

### Step 1: Run maximal EFA (without and with rotation)

```{r s2 all no rotation}
# examine scree plot
fa.parallel(d2_all)

# run EFA without rotation with N factors
efa_d2_all_unrotated <- fa(d2_all, 13, rotate = "none",
                           cor = chosenCorType, fm = "minres")
print(efa_d2_all_unrotated)

# examine eigenvalues and variance explained
efa_d2_all_unrotated_eigenvalues <- print(efa_d2_all_unrotated)$Vaccounted %>%
  t() %>%
  data.frame()

# count factors with eigenvalues > 1 and variance explained > 5%
efa_d2_all_unrotated_nfactors <- efa_d2_all_unrotated_eigenvalues %>%
  filter(SS.loadings > 1, Proportion.Explained > 0.05) %>%
  count() %>%
  as.numeric()
efa_d2_all_unrotated_nfactors
```

```{r s2 all rotation}
efa_d2_all_rotated <- fa(d2_all, efa_d2_all_unrotated_nfactors, rotate = chosenRotType,
                           cor = chosenCorType, fm = "minres")

# check that each of these factors is the dominant factor for at least one mental capacity item
efa_d2_all_rotated_loadings <- fa.sort(loadings(efa_d2_all_rotated)[]) %>%
  data.frame() %>%
  rownames_to_column("capacity") %>%
  gather(factor, loading, -capacity) %>%
  mutate(loading_abs = abs(loading)) %>%
  group_by(capacity) %>%
  top_n(1, loading_abs) %>%
  ungroup()
efa_d2_all_rotated_loadings

# drop any factors where n < 1
efa_d2_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0)

# set number of factors to extract
nfactors_d2_all <- efa_d2_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0) %>%
  nrow()
nfactors_d2_all
```

### Step 2: Run EFA with varimax rotation

```{r s2 all varimax rotation}
# run EFA with rotation with N factors
efa_d2_all_rotatedN <- fa(d2_all, nfactors_d2_all, 
                          rotate = chosenRotType, cor = chosenCorType, fm = "minres")
print(efa_d2_all_rotatedN)

# get loadings for each factor
efa_d2_all_rotatedN_loadings <- loadings(efa_d2_all_rotatedN)[] %>%
  data.frame() %>% 
  rownames_to_column(var = "mc")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

#### Factor loadings table

```{r s2 loadings table}
data.frame(loadings(fa.sort(efa_d2_all_rotatedN))[]) %>%
  rownames_to_column("capacity") %>%
  mutate_at(vars(starts_with("M")), funs(round2))
```

# Study 3 

Design: 2 conditions (beetle, robot), within-subjects
Date conducted: 2016-01-10

## Demographics

```{r s3 demographics}
# examine exclusion
excludedCounts("study 3")

# make demographics tables
demoSampleSize("study 3")
demoDuration("study 3")
demoAge("study 3")
demoGender("study 3")
demoRace("study 3")
demoReligion("study 3")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

## Exploratory factor analysis

### Step 1: Run maximal EFA (without and with rotation)

```{r s3 all no rotation}
# examine scree plot
fa.parallel(d3_all)

# run EFA without rotation with N factors
efa_d3_all_unrotated <- fa(d3_all, 13, rotate = "none",
                           cor = chosenCorType, fm = "minres")
print(efa_d3_all_unrotated)

# examine eigenvalues and variance explained
efa_d3_all_unrotated_eigenvalues <- print(efa_d3_all_unrotated)$Vaccounted %>%
  t() %>%
  data.frame()

# count factors with eigenvalues > 1 and variance explained > 5%
efa_d3_all_unrotated_nfactors <- efa_d3_all_unrotated_eigenvalues %>%
  filter(SS.loadings > 1, Proportion.Explained > 0.05) %>%
  count() %>%
  as.numeric()
efa_d3_all_unrotated_nfactors
```

```{r s3 all rotation}
efa_d3_all_rotated <- fa(d3_all, efa_d3_all_unrotated_nfactors, rotate = chosenRotType,
                           cor = chosenCorType, fm = "minres")

# check that each of these factors is the dominant factor for at least one mental capacity item
efa_d3_all_rotated_loadings <- fa.sort(loadings(efa_d3_all_rotated)[]) %>%
  data.frame() %>%
  rownames_to_column("capacity") %>%
  gather(factor, loading, -capacity) %>%
  mutate(loading_abs = abs(loading)) %>%
  group_by(capacity) %>%
  top_n(1, loading_abs) %>%
  ungroup()
efa_d3_all_rotated_loadings

# drop any factors where n < 1
efa_d3_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0)

# set number of factors to extract
nfactors_d3_all <- efa_d3_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0) %>%
  nrow()
nfactors_d3_all
```

### Step 2: Run EFA with varimax rotation

```{r s3 all varimax rotation}
# run EFA with rotation with N factors
efa_d3_all_rotatedN <- fa(d3_all, nfactors_d3_all, 
                          rotate = chosenRotType, cor = chosenCorType, fm = "minres")
print(efa_d3_all_rotatedN)

# get loadings for each factor
efa_d3_all_rotatedN_loadings <- loadings(efa_d3_all_rotatedN)[] %>%
  data.frame() %>% 
  rownames_to_column(var = "mc")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

#### Factor loadings table

```{r s3 loadings table}
data.frame(loadings(fa.sort(efa_d3_all_rotatedN))[]) %>%
  rownames_to_column("capacity") %>%
  mutate_at(vars(starts_with("M")), funs(round2))
```

# Study 4

Design: 21 conditions, between-subjects
Date conducted: 2016-01-14

## Demographics

```{r s4 demographics}
# examine exclusion
excludedCounts("study 4")

# make demographics tables
demoSampleSize("study 4")
demoDuration("study 4")
demoAge("study 4")
demoGender("study 4")
demoRace("study 4")
demoEducation("study 4")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

## Exploratory factor analysis

### Step 1: Run maximal EFA (without and with rotation)

```{r s4 all no rotation}
# examine scree plot
fa.parallel(d4_all)

# run EFA without rotation with N factors
efa_d4_all_unrotated <- fa(d4_all, 13, rotate = "none",
                           cor = chosenCorType, fm = "minres")
print(efa_d4_all_unrotated)

# examine eigenvalues and variance explained
efa_d4_all_unrotated_eigenvalues <- print(efa_d4_all_unrotated)$Vaccounted %>%
  t() %>%
  data.frame()

# count factors with eigenvalues > 1 and variance explained > 5%
efa_d4_all_unrotated_nfactors <- efa_d4_all_unrotated_eigenvalues %>%
  filter(SS.loadings > 1, Proportion.Explained > 0.05) %>%
  count() %>%
  as.numeric()
efa_d4_all_unrotated_nfactors
```

```{r s4 all rotation}
efa_d4_all_rotated <- fa(d4_all, efa_d4_all_unrotated_nfactors, rotate = chosenRotType,
                           cor = chosenCorType, fm = "minres")

# check that each of these factors is the dominant factor for at least one mental capacity item
efa_d4_all_rotated_loadings <- fa.sort(loadings(efa_d4_all_rotated)[]) %>%
  data.frame() %>%
  rownames_to_column("capacity") %>%
  gather(factor, loading, -capacity) %>%
  mutate(loading_abs = abs(loading)) %>%
  group_by(capacity) %>%
  top_n(1, loading_abs) %>%
  ungroup()
efa_d4_all_rotated_loadings

# drop any factors where n < 1
efa_d4_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0)

# set number of factors to extract
nfactors_d4_all <- efa_d4_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0) %>%
  nrow()
nfactors_d4_all
```

### Step 2: Run EFA with varimax rotation

```{r s4 all varimax rotation}
# run EFA with rotation with N factors
efa_d4_all_rotatedN <- fa(d4_all, 4, 
                          rotate = chosenRotType, cor = chosenCorType, fm = "minres")
print(efa_d4_all_rotatedN)

# get loadings for each factor
efa_d4_all_rotatedN_loadings <- loadings(efa_d4_all_rotatedN)[] %>%
  data.frame() %>% 
  rownames_to_column(var = "mc")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

#### Factor loadings table

```{r s4 loadings table}
data.frame(loadings(fa.sort(efa_d4_all_rotatedN))[]) %>%
  rownames_to_column("capacity") %>%
  mutate_at(vars(starts_with("M")), funs(round2))
```

# Big factor loadings table for all studies (Studies 1-4)

```{r all studies loadings table}
order_s1 <- loadings(fa.sort(efa_d1_all_rotatedN))[] %>%
  data.frame() %>%
  rownames_to_column(var = "mc") %>%
  rownames_to_column(var = "order1") %>%
  rename(s1_MR2 = MR2, s1_MR1 = MR1, s1_MR3 = MR3)

order_s2 <- loadings(fa.sort(efa_d2_all_rotatedN))[] %>%
  data.frame() %>%
  rownames_to_column(var = "mc") %>%
  rename(s2_MR2 = MR2, s2_MR1 = MR1, s2_MR3 = MR3)

order_s3 <- loadings(fa.sort(efa_d3_all_rotatedN))[] %>%
  data.frame() %>%
  rownames_to_column(var = "mc") %>%
  rename(s3_MR2 = MR2, s3_MR1 = MR1, s3_MR3 = MR3)

order_s4 <- loadings(fa.sort(efa_d4_all_rotatedN))[] %>%
  data.frame() %>%
  rownames_to_column(var = "mc") %>%
  rename(s4_MR2 = MR2, s4_MR1 = MR1, s4_MR3 = MR3)

bigTable <- order_s1 %>%
  full_join(order_s2) %>%
  full_join(order_s3) %>%
  full_join(order_s4) %>%
  mutate_at(vars(starts_with("s")), funs(round2)) %>%
  select(order1, mc, ends_with("MR1"), ends_with("MR2"), ends_with("MR3"))

bigTable
```

# Figures

```{r plotting setup character means}
# bootstrap 95% CIs for ratings by character (nonparametric)
# study 1
char_plotting_ratings_s1 <- d1 %>% 
  select(condition, subid, happy:pride) %>%
  gather(mc, response, -subid, -condition) %>%
  mutate(response = as.numeric(response)) %>%
  multi_boot(column = "response",
             summary_function = "mean",
             summary_groups = c("condition", "mc"),
             statistics_functions = c("ci_lower", "mean", "ci_upper")) %>%
  full_join(demoSampleSize("study 1") %>% filter(condition != "all")) %>%
  mutate(study = "study 1")

# study 2
char_plotting_ratings_s2 <- d2 %>% 
  select(condition, subid, happy:pride) %>%
  gather(mc, response, -subid, -condition) %>%
  mutate(response = as.numeric(response)) %>%
  multi_boot(column = "response",
             summary_function = "mean",
             summary_groups = c("condition", "mc"),
             statistics_functions = c("ci_lower", "mean", "ci_upper")) %>%
  full_join(demoSampleSize("study 2") %>% filter(condition != "all")) %>%
  mutate(study = "study 2")


# study 3
char_plotting_ratings_s3 <- d3 %>% 
  select(target, subid, happy:pride) %>%
  rename(condition = target) %>%
  gather(mc, response, -subid, -condition) %>%
  mutate(response = as.numeric(response)) %>%
  multi_boot(column = "response",
             summary_function = "mean",
             summary_groups = c("condition", "mc"),
             statistics_functions = c("ci_lower", "mean", "ci_upper")) %>%
  full_join(demoSampleSize("study 3") %>% filter(condition != "all")) %>%
  mutate(study = "study 3")

# study 4
char_plotting_ratings_s4 <- d4 %>% 
  select(condition, subid, happy:pride) %>%
  gather(mc, response, -subid, -condition) %>%
  mutate(response = as.numeric(response)) %>%
  multi_boot(column = "response",
             summary_function = "mean",
             summary_groups = c("condition", "mc"),
             statistics_functions = c("ci_lower", "mean", "ci_upper")) %>%
  full_join(demoSampleSize("study 4") %>% filter(condition != "all")) %>%
  mutate(study = "study 4")
```

```{r plotting setup character means merge}
char_plotting_ratings_all <- char_plotting_ratings_s1 %>%
  full_join(char_plotting_ratings_s2) %>%
  full_join(char_plotting_ratings_s3) %>%
  full_join(char_plotting_ratings_s4) %>%
  ungroup() %>%
  mutate(study = factor(study),
         condition = factor(condition,
                            levels = c("stapler", "car", "computer", "robot",
                                       "microbe", "beetle", "fish", "bluejay",
                                       "frog", "mouse", "goat", "dog",
                                       "bear", "dolphin", "elephant", "chimp",
                                       "fetus", "pvs", "infant", "child", "adult")))
```

```{r plotting setup capacity wordings}
char_plotting_wordings <- char_plotting_ratings_all %>%
  ungroup() %>%
  select(mc) %>%
  distinct() %>% 
  mutate(wording = factor(
    recode(mc,
           hungry = "getting hungry", pain = "experiencing pain",
           tired = "feeling tired", fear = "experiencing fear",
           computations = "doing computations", pleasure = "experiencing pleasure",
           conscious = "being conscious", free_will = "having free will",
           safe = "feeling safe", desires = "having desires",
           calm = "feeling calm", nauseated = "feeling nauseated",
           angry = "getting angry", intentions = "having intentions",
           self_aware = "being self-aware", odors = "detecting odors",
           embarrassed = "feeling embarrassed", pride = "experiencing pride",
           love = "feeling love", guilt = "experiencing guilt",
           depressed = "feeling depressed", disrespected = "feeling disrespected",
           beliefs = "holding beliefs", emo_recog = "understanding ... feeling",
           joy = "experiencing joy", personality = "having a personality",
           happy = "feeling happy", morality = "telling right from wrong",
           thoughts = "having thoughts", self_restraint = "exercising self-restraint",
           remembering = "remembering things", recognizing = "recognizing others",
           temperature = "sensing temperatures", communicating = "communicating ...",
           goal = "working toward a goal", depth = "perceiving depth",
           sounds = "detecting sounds", seeing = "seeing things",
           choices = "making choices", reasoning = "reasoning about things")))
```


```{r plotting setup loadings}
# merge with loadings, orderings, and dominant factors from each study 
char_plotting <- char_plotting_ratings_all %>%
  full_join(char_plotting_wordings) %>%
  full_join(order_s1 %>%
              mutate(s1_MR1_abs = abs(s1_MR1),
                     s1_MR2_abs = abs(s1_MR2),
                     s1_MR3_abs = abs(s1_MR3),
                     s1_factor = 
                       ifelse(s1_MR1_abs > s1_MR2_abs &
                                s1_MR1_abs > s1_MR3_abs, "BODY",
                              ifelse(s1_MR2_abs > s1_MR1_abs &
                                       s1_MR2_abs > s1_MR3_abs, "HEART",
                                     ifelse(s1_MR3_abs > s1_MR1_abs &
                                              s1_MR3_abs > s1_MR2_abs, "MIND",
                                            NA))),
                     s1_color = recode(s1_factor,
                                       "BODY" = "#377EB8",
                                       "HEART" = "#4DAF4A",
                                       "MIND" = "#E41A1C"),
                     s1_order = as.numeric(order1)) %>%
              select(-s1_MR1_abs, -s1_MR2_abs, -s1_MR3_abs)) %>%
  full_join(order_s2 %>%
              data.frame() %>%
              mutate(s2_MR1_abs = abs(s2_MR1),
                     s2_MR2_abs = abs(s2_MR2),
                     s2_MR3_abs = abs(s2_MR3),
                     s2_factor = 
                       ifelse(s2_MR1_abs > s2_MR2_abs &
                                s2_MR1_abs > s2_MR3_abs, "BODY",
                              ifelse(s2_MR2_abs > s2_MR1_abs &
                                       s2_MR2_abs > s2_MR3_abs, "HEART",
                                     ifelse(s2_MR3_abs > s2_MR1_abs &
                                              s2_MR3_abs > s2_MR2_abs, "MIND",
                                            NA))),
                     s2_color = recode(s2_factor,
                                       "BODY" = "#377EB8",
                                       "HEART" = "#4DAF4A",
                                       "MIND" = "#E41A1C")) %>%
              rownames_to_column(var = "s2_order") %>%
              mutate(s2_order = as.numeric(s2_order)) %>%
              select(-s2_MR1_abs, -s2_MR2_abs, -s2_MR3_abs)) %>%
  full_join(order_s3 %>%
              mutate(s3_MR1_abs = abs(s3_MR1),
                     s3_MR2_abs = abs(s3_MR2),
                     s3_MR3_abs = abs(s3_MR3),
                     s3_factor = 
                       ifelse(s3_MR1_abs > s3_MR2_abs &
                                s3_MR1_abs > s3_MR3_abs, "BODY",
                              ifelse(s3_MR2_abs > s3_MR1_abs &
                                       s3_MR2_abs > s3_MR3_abs, "HEART",
                                     ifelse(s3_MR3_abs > s3_MR1_abs &
                                              s3_MR3_abs > s3_MR2_abs, "MIND",
                                            NA))),
                     s3_color = recode(s3_factor,
                                       "BODY" = "#377EB8",
                                       "HEART" = "#4DAF4A",
                                       "MIND" = "#E41A1C")) %>%
              rownames_to_column(var = "s3_order") %>%
              mutate(s3_order = as.numeric(s3_order)) %>%
              select(-s3_MR1_abs, -s3_MR2_abs, -s3_MR3_abs)) %>%
  full_join(order_s4 %>%
              mutate(s4_MR1_abs = abs(s4_MR1),
                     s4_MR2_abs = abs(s4_MR2),
                     s4_MR3_abs = abs(s4_MR3),
                     s4_factor = 
                       ifelse(s4_MR1_abs > s4_MR2_abs &
                                s4_MR1_abs > s4_MR3_abs, "BODY",
                              ifelse(s4_MR2_abs > s4_MR1_abs &
                                       s4_MR2_abs > s4_MR3_abs, "HEART",
                                     ifelse(s4_MR3_abs > s4_MR1_abs &
                                              s4_MR3_abs > s4_MR2_abs, "MIND",
                                            NA))),
                     s4_color = recode(s4_factor,
                                       "BODY" = "#377EB8",
                                       "HEART" = "#4DAF4A",
                                       "MIND" = "#E41A1C")) %>%
              rownames_to_column(var = "s4_order") %>%
              mutate(s4_order = as.numeric(s4_order)) %>%
              select(-s4_MR1_abs, -s4_MR2_abs, -s4_MR3_abs))

# configure plot labels
label_df_s1 <- char_plotting %>% filter(study == "study 1") %>% select(condition, n) %>% unique()
label_df_s2 <- char_plotting %>% filter(study == "study 2") %>% select(condition, n) %>% unique()
label_df_s3 <- char_plotting %>% filter(study == "study 3") %>% select(condition, n) %>% unique()
label_df_s4 <- char_plotting %>% filter(study == "study 4") %>% select(condition, n) %>% unique()

facetLabs_s1 <- gsub(" \\(", "\n(", makeFacetLabs(char_plotting %>% filter(study == "study 1")))
facetLabs_s2 <- gsub(" \\(", "\n(", makeFacetLabs(char_plotting %>% filter(study == "study 2")))
facetLabs_s3 <- gsub(" \\(", "\n(", makeFacetLabs(char_plotting %>% filter(study == "study 3")))
facetLabs_s4 <- gsub(" \\(", "\n(", makeFacetLabs(char_plotting %>% filter(study == "study 4")))

# configure custom palette
myPalette <- brewer.pal(3, "Set1"); names(myPalette) <- c("BODY", "HEART", "MIND")

palette_base <- char_plotting %>% 
  select(mc, ends_with("_factor")) %>%
  distinct()
palette_s1 <- c(rep(myPalette["MIND"], palette_base %>% filter(s1_factor == "MIND") %>% count()),
                rep(myPalette["HEART"], palette_base %>% filter(s1_factor == "HEART") %>% count()),
                rep(myPalette["BODY"], palette_base %>% filter(s1_factor == "BODY") %>% count()))
palette_s2 <- c(rep(myPalette["MIND"], palette_base %>% filter(s2_factor == "MIND") %>% count()),
                rep(myPalette["HEART"], palette_base %>% filter(s2_factor == "HEART") %>% count()),
                rep(myPalette["BODY"], palette_base %>% filter(s2_factor == "BODY") %>% count()))
palette_s3 <- c(rep(myPalette["MIND"], palette_base %>% filter(s3_factor == "MIND") %>% count()),
                rep(myPalette["HEART"], palette_base %>% filter(s3_factor == "HEART") %>% count()),
                rep(myPalette["BODY"], palette_base %>% filter(s3_factor == "BODY") %>% count()))
palette_s4 <- c(rep(myPalette["MIND"], palette_base %>% filter(s4_factor == "MIND") %>% count()),
                rep(myPalette["HEART"], palette_base %>% filter(s4_factor == "HEART") %>% count()),
                rep(myPalette["BODY"], palette_base %>% filter(s4_factor == "BODY") %>% count()))
```

## Figure 1

Mean ratings of 40 mental capacities for a subset of the 21 entities included in Study 4. (See Fig. S5 for mean ratings for the full set of entities.) Participants responded on a scale from 0 (Not at all capable) to 6 (Highly capable). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 1. *Doing computations* was the only item to load negatively on its dominant factor in any study (and did so in Studies 1-3); in Study 4, it loaded positively on its dominant factor (Factor 2, “heart”).

```{r figure 1, fig.width = 18, fig.height = 10}
# make dataframe
s4_subset_plotting <- char_plotting %>% 
  filter(study == "study 4",
         condition %in% c("stapler", "robot", "beetle", "goat", "elephant", "adult")) %>%
  distinct()

# plot! (ordered by study 1 factor loadings)
s4_subset <- ggplot(s4_subset_plotting, 
               aes(x = mean, y = reorder(wording, desc(s1_order)), colour = s1_color)) +
  geom_point(stat = "identity", position = "identity", size = 4) +
  geom_errorbarh(aes(xmin = ci_lower, xmax = ci_upper), height = 0.4) +
  facet_wrap(~ condition, ncol = 6,
             labeller = labeller(condition = facetLabs_s4)) +
  theme_bw() +
  theme(text = element_text(size = 16),
        axis.title.y = element_blank(),
        axis.text.y = element_text(face = "italic",
                                   colour = palette_s1),
        panel.grid.minor = element_blank(),
        legend.position = "none") +
  scale_x_continuous(name = "\nMean rating",
                     limits = c(-3, 3),
                     breaks = seq(-3, 3, 1),
                     labels = seq(0, 6, 1)) +
  scale_colour_brewer(name = "Factor:",
                      type = "qual", palette = 6)

s4_subset
```

## Figure S1 (supporting materials)

Factor loadings for the 40 mental capacities on the three rotated factors in Study 1. Items are colored by their dominant factor loading: Items that loaded most strongly on the body factor (physiological states and will) are in red; items that loaded most strongly on the heart factor (social-emotional experiences and morality) are in blue; and items that loaded most strongly on the mind factor (perceptual-cognitive abilities and goal pursuit) are in green.

```{r figure S1}
# set up labels for plot (shortened version of mental capacity items)
wording <- loadings(efa_d1_all_rotatedN)[] %>%
  data.frame() %>%
  rownames_to_column(var = "item") %>%
  select(item) %>%
  mutate(wording = c("feeling happy", "feeling depressed", "experiencing fear",
                     "getting angry", "feeling calm", "detecting sounds",
                     "seeing things", "sensing temperatures", "detecting odors",
                     "perceiving depth", "doing computations", "having thoughts",
                     "reasoning about things", "remembering things", "holding beliefs",
                     "getting hungry", "feeling tired", "experiencing pain",
                     "feeling nauseated", "feeling safe", "feeling love",
                     "recognizing someone", "communicating with others", "experiencing guilt",
                     "feeling disrespected", "having free will", "making choices",
                     "exercising self-restraint", "having intentions", "working toward a goal",
                     "being conscious", "being self-aware", "having desires",
                     "feeling embarrassed", "understanding how others are feeling", 
                     "experiencing joy", "telling right from wrong", "having a personality",
                     "experiencing pleasure", "experiencing pride"),
         short = c("happiness", "depression", "fear",
                   "anger", "calm", "sound",
                   "sight", "temperature", "odor",
                   "depth", "computation", "thought",
                   "reasoning", "memory", "belief",
                   "hunger", "tiredness", "pain",
                   "nausea", "safety", "love",
                   "recognition", "communication", "guilt",
                   "disrespect", "free will", "choice",
                   "self-restraint", "intention", "goal",
                   "consciousness", "self-awareness", "desire",
                   "embarrassment", "empathy", 
                   "joy", "morality", "personality",
                   "pleasure", "pride"))

# make dataframe for plotting
scatter_plotting <- loadings(efa_d1_all_rotatedN)[] %>%
  data.frame() %>%
  rownames_to_column(var = "item") %>%
  rename(BODY = MR1,
         HEART = MR2,
         MIND = MR3) %>%
  full_join(wording) %>%
  mutate(dominant = factor(
    ifelse(pmax(abs(BODY), abs(HEART), abs(MIND)) == abs(BODY), "BODY",
           ifelse(pmax(abs(BODY), abs(HEART), abs(MIND)) == abs(HEART), "HEART",
                  ifelse(pmax(abs(BODY), abs(HEART), abs(MIND)) == abs(MIND), "MIND",
                         NA)))),
    size = ifelse(pmax(abs(BODY), abs(HEART), abs(MIND)) == abs(BODY), abs(BODY),
                  ifelse(pmax(abs(BODY), abs(HEART), abs(MIND)) == abs(HEART), abs(HEART),
                         ifelse(pmax(abs(BODY), abs(HEART), abs(MIND)) == abs(MIND), abs(MIND),
                                NA))),
    color = ifelse(dominant == "BODY", "#e41a1c",
                   ifelse(dominant == "HEART", "#377eb8",
                          ifelse(dominant == "MIND", "#4daf4a",
                                 NA))))

# plot!
figS1 <- plot_ly(scatter_plotting, x = HEART, y = BODY, z = MIND,
             type = "scatter3d",
             color = dominant, colors = c("#4daf4a", "#e41a1c", "#377eb8"),
             marker = list(size = 4),
             text = short,
             textfont = list(size = 15),
             mode = "text+markers",
             showlegend = TRUE)

figS1
```

## Figure S2 (supporting materials)

Mean ratings of 40 mental capacities for the 2 entities included in Studies 1-3. Participants responded on a scale from 0 (Not at all capable) to 6 (Highly capable). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 1.

```{r figure S2, fig.width = 27, fig.height = 20}
# make dataframe
s123_plotting <- char_plotting %>% 
  filter(study != "study 4") %>%
  distinct()

# plot! (ordered by study 3 factor loadings)
s123 <- ggplot(s123_plotting, 
               aes(y = mean, x = reorder(wording, desc(s1_order)), colour = s1_color,
                   shape = study)) +
  geom_point(stat = "identity", position = position_dodge(width = 0.6), size = 2) +
  geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper), width = 0.4,
                position = position_dodge(width = 0.6)) +
  facet_wrap(~ condition) +
  theme_bw() +
  scale_y_continuous(name = "\nMean rating",
                     limits = c(-3, 3),
                     breaks = seq(-3, 3, 1),
                     labels = seq(0, 6, 1)) +
  scale_shape_discrete(name = "Study:") +
  scale_colour_brewer(name = "Factor:",
                      type = "qual", palette = 6,
                      guide = FALSE) +
  coord_flip() +
  theme(text = element_text(size = 16),
      axis.title.y = element_blank(),
      axis.text.y = element_text(face = "italic",
                                 colour = palette_s1),
      panel.grid.minor = element_blank(),
      legend.position = "right")

s123
```

## Figure S3 (supporting materials)

Mean ratings of 40 mental capacities for all 21 entities included in Study 4. Participants responded on a scale from 0 (Not at all capable) to 6 (Highly capable). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 4.

```{r figure S3, fig.width = 27, fig.height = 20}
# make dataframe
s4_plotting <- char_plotting %>% 
  filter(study == "study 4") %>%
  distinct()

# plot! (ordered by study 4 factor loadings)
s4 <- ggplot(s4_plotting, 
               aes(x = mean, y = reorder(wording, desc(s4_order)), colour = s4_color)) +
  geom_point(stat = "identity", position = "identity", size = 4) +
  geom_errorbarh(aes(xmin = ci_lower, xmax = ci_upper), height = 0.4) +
  facet_wrap(~ condition, ncol = 21,
             labeller = labeller(condition = facetLabs_s4)) +
  theme_bw() +
  theme(text = element_text(size = 16),
        axis.title.y = element_blank(),
        axis.text.y = element_text(face = "italic",
                                   colour = palette_s4),
        panel.grid.minor = element_blank(),
        legend.position = "none") +
  scale_x_continuous(name = "\nMean rating",
                     limits = c(-3, 3),
                     breaks = seq(-3, 3, 1),
                     labels = seq(0, 6, 1)) +
  scale_colour_brewer(name = "Factor:",
                      type = "qual", palette = 6)

s4
```

## Figure S7 (supporting materials)

Mean ratings of 40 mental capacities for all of the entities included in Studies 1-4. Participants responded on a scale from 0 (Not at all capable) to 6 (Highly capable). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 1.

```{r figure S7, fig.width = 27, fig.height = 40}
# make dataframe
s1234_plotting <- char_plotting %>% 
  distinct()

# plot! (ordered by study 3 factor loadings)
s1234 <- ggplot(s1234_plotting, 
               aes(y = mean, x = reorder(wording, desc(s1_order)), colour = s1_color,
                   shape = study)) +
  geom_point(stat = "identity", position = position_dodge(width = 0.6), size = 2) +
  geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper), width = 0.4,
                position = position_dodge(width = 0.6)) +
  facet_wrap(~ condition, ncol = 11) +
  theme_bw() +
  scale_y_continuous(name = "\nMean rating",
                     limits = c(-3, 3),
                     breaks = seq(-3, 3, 1),
                     labels = seq(0, 6, 1)) +
  scale_shape_discrete(name = "Study:") +
  scale_colour_brewer(name = "Factor:",
                      type = "qual", palette = 6,
                      guide = FALSE) +
  coord_flip() +
  theme(text = element_text(size = 16),
      axis.title.y = element_blank(),
      axis.text.y = element_text(face = "italic",
                                 colour = palette_s1),
      panel.grid.minor = element_blank(),
      legend.position = "bottom")

s1234
```

